Systems, Methods and Devices for Achieving Glycemic Balance

ABSTRACT

Systems, methods and/or devices for optimizing a patient&#39;s insulin dosage regimen over time, comprising at least a first memory for storing data inputs corresponding at least to one or more components in a patient&#39;s present insulin dosage regimen, and data inputs corresponding at least to the patient&#39;s blood-glucose-level measurements determined at a plurality of times, and a processor operatively connected to the at least first memory. The processor is programmed at least to determine from the data inputs corresponding to the patient&#39;s blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one of the one or more components in the patient&#39;s present insulin dosage regimen. Also disclosed are systems, methods and/or devices for treating a patient&#39;s diabetes by providing treatment guidance, wherein the patient&#39;s current glycemic state is determined relative to a desired balance point; and determining from at least one of a plurality of the data corresponding to the patient&#39;s blood glucose-level measurements whether and by how much to vary at least one of the one or more components in the patient&#39;s present insulin dosage regimen to get closer to the patient&#39;s desired balance point; wherein the desired balance point is the patient&#39;s lowest blood glucose-level within a predetermined range achievable before increasing the frequency of hypoglycemic events above a predetermined threshold.

RELATED DOCUMENTS

The application is a continuation of U.S. patent application Ser. No.13/168,659, filed Jun. 24, 2011, which is a continuation in part of U.S.patent application Ser. No. 12/417,955, filed Apr. 3, 2009, which claimsthe benefit of priority from, U.S. provisional application Ser. No.61/042,487, filed 4 Apr. 2008, and U.S. provisional application Ser. No.61/060,645, filed 11 Jun. 2008. The application is also a continuationin part of U.S. patent application Ser. No. 12/417,960, filed Apr. 3,2009, which claims the benefit of priority from, U.S. provisionalapplication Ser. No. 61/042,487, filed 4 Apr. 2008, and U.S. provisionalapplication Ser. No. 61/060,645, filed 11 Jun. 2008. The disclosure ofeach of these applications is incorporated herein by reference in itsentirety.

In addition, the present application is related to PCT/US2009/039421,filed Apr. 3, 2009; PCT/US2009/039418, filed Apr. 3, 2009; U.S. patentapplication Ser. No. 61/113,252, filed Nov. 11, 2008; U.S. patentapplication Ser. No. 61/257,866, filed Nov. 4, 2009; PCT/US2009/063989,filed Nov. 11, 2009; U.S. patent application Ser. No. 61/257,886 filedNov. 4, 2009; U.S. patent application Ser. No. 12/926,234, filed Nov. 3,2010; and PCT/US2010/055246, filed Nov. 3, 2010. Each of theseapplications is incorporated herein by reference in its entirety.Finally, the reference “Convex Optimization” by Boyd and Vandenberghe(Cambridge University Press, 2004; ISBN-10: 0521833787), is herebyincorporated by reference in its entirety.

FIELD

The present disclosure relates to systems, methods and/or devices foroptimizing the insulin dosage regimen for a diabetes patient, and moreparticularly to such systems, methods and/or devices according to whicha processor is programmed at least to determine from the data inputscorresponding to the patient's blood-glucose-level measurementsdetermined at a plurality of times whether and by how much to vary atleast one of the one or more components in the patient's present insulindosage regimen in order to get closer to the patient's desired balancepoint; wherein the desired balance point, for example, is the patient'slowest blood glucose-level within a predetermined range achievablebefore increasing the frequency of hypoglycemic events above apredetermined threshold.

BACKGROUND

Diabetes is a chronic disease resulting from deficient insulin secretionby the endocrine pancreas. About 7% of the general population in theWestern Hemisphere suffers from diabetes. Of these persons, roughly 90%suffer from Type-2 diabetes while approximately 10% suffer from Type-1.In Type-1 diabetes, patients effectively surrender their endocrinepancreas to autoimmune distraction and so become dependent on dailyinsulin injections to control blood-glucose-levels. In Type-2 diabetes,on the other hand, the endocrine pancreas gradually fails to satisfyincreased insulin demands, thus requiring the patient to compensate witha regime of oral medications or insulin therapy. In the case of eitherType-1 or Type-2 diabetes, the failure to properly control glucoselevels in the patient may lead to such complications as heart attacks,strokes, blindness, renal failure, and even premature death.

Diabetes is a metabolic disorder where the individual's ability tosecrete insulin, and therefore to regulate glucose level has beencompromised. For a non-diabetic person, normal glucose levels aretypically around 85-110 mg/dl, and can spike after meals to typicallyaround 140-200 mg/dl. Glucose levels can range from hypo- tohyper-glycemia. Low glucose levels or hypoglycemia can drop belowlife-sustaining level and lead to seizures, consciousness-loss, and evendeath. Hyperglycemia over a long period of time has been associated withfar increased chances to develop diabetes related complications such asheart disease, hypertension, kidney disease, and blindness among others.

Insulin therapy is the mainstay of Type-1 diabetes management and one ofthe most widespread treatments in Type-2 diabetes, about 27% of thesufferers of which require insulin. Insulin administration is designedto imitate physiological insulin secretion by introducing two classes ofinsulin into the patient's body: Long-acting insulin, which fulfillsbasal metabolic needs; and short-acting insulin (also known asfast-acting insulin), which compensates for sharp elevations inblood-glucose-levels following patient meals. Orchestrating the processof dosing these two types of insulin, in whatever form (e.g., separatelyor as premixed insulin) involves numerous considerations.

First, patients measure their blood-glucose-levels (using some form of aglucose meter) on average about 3 to 4 times per day. The number of suchmeasurements and the variations therebetween complicates theinterpretation of these data, making it difficult to extrapolate trendstherefrom that may be employed to better maintain the disease. Second,the complexity of human physiology continuously imposes changes ininsulin needs for which frequent insulin dosage regimen adjustments arewarranted. Presently, these considerations are handled by a patient'sendocrinologist or other healthcare professional during clinicappointments. Unfortunately, these visits are relativelyinfrequent—occurring once every 3 to 6 months—and of short duration, sothat the physician or other healthcare professional is typically onlyable to review the very latest patient medical data. In consequence, ithas been shown that more than 60% of patients control their diabetes atsub-optimal levels, leading to unwanted complications from the disease.

Indeed, one of the major obstacles of diabetes management is the lack ofavailability of a patient's healthcare professional and the relativeinfrequency of clinic appointments. Studies have, in fact, establishedthat more frequent insulin dosage regimen adjustments, for example,every 1 to 2 weeks—improves diabetes control in most patients. Yet asthe number of diabetes sufferers continues to expand, it is expectedthat the possibility of more frequent insulin dosage regimen adjustmentsvia increased clinic visits will, in fact, decrease. And, unfortunately,conventional diabetes treatment solutions do not address this obstacle.

The device most commonly employed in diabetes management is the glucosemeter. Such devices come in a variety of forms, although most arecharacterized by their ability to provide patients near instantaneousreadings of their blood-glucose-levels. This additional information canbe used to better identify dynamic trends in blood-glucose-levels.However, conventional glucose meters are designed to be diagnostic toolsrather than therapeutic ones. Therefore, by themselves, evenstate-of-the-art glucose meters do not lead to improved glycemiccontrol.

One conventional solution to the treatment of diabetes is the insulinpump. Insulin pumps are devices that continuously infuse short actinginsulin into a patient at a predetermined rate to cover both basal needsand meals. As with manual insulin administration therapy, a healthcareprofessional sets the pump with the patient's insulin dosage regimenduring clinic visits. In addition to their considerable current expense,which prohibits their widespread use by patients with Type-2 diabetes,insulin pumps require frequent adjustment by the physician or otherhealthcare professional to compensate for the needs of individualpatients based upon frequent blood-glucose-level measurements.

An even more recent solution to diabetes treatment seeks to combine aninsulin pump and near-continuous glucose monitoring in an effort tocreate, in effect, an artificial pancreas regulating a patient'sblood-glucose-level with infusions of short-acting insulin. According tothis solution, real-time patient information is employed to matchinsulin dosing to the patient's dynamic insulin needs irrespective ofany underlying physician-prescribed treatment plan. While such systemsaddress present dosing requirements, they are entirely reactive and notinstantaneously effective. In consequence of these drawbacks, suchcombined systems are not always effective at controlling blood glucoselevels. For instance, such combined units cannot forecast unplannedactivities, such as exercise, that may excessively lower a patient'sblood-glucose level. And when the hypoglycemic condition is detected,the delay in the effectiveness of the insulin occasioned not only by thenature of conventional synthetic insulin but also the sub-dermaldelivery of that insulin by conventional pumps results in inefficientcorrection of the hypoglycemic event.

The most common biomarker used to access glycemic control is hemoglobinA1C (A1C for brevity). The relationship between average glucose levelsand A1C has been studied. For healthy individuals A1C is between 4.6%and 5.8%, for people with diabetes the American Diabetes Association(ADA) and the European Association for the Study of Diabetes (EASD)recommend maintaining A1C<7% that correlates to an average glucose levelbelow 150 mg/dl.

Studies have demonstrated the relationship between A1C and complication.The ADA and EASD have set the goal of getting A1C to below 7%. This waschosen as a compromise between lowering the risk for developingcomplications and the risk of severe (and potentially fatal)hypoglycemia. As a result, diabetes management has developed with itsmain goal being to bring A1C down as reflected by several consensusstatements issued by various authorities. Up until recently, littleattention has been devoted to the other side of the equation beingprevention of hypoglycemia. It is assumed that hypoglycemia is a sideeffect of insulin, or oral anti-diabetes drugs (OAD), therapy as whenmean glucose decreases one's chances of seeing more low glucose levelsincreases. Since lowering A1C and avoiding hypoglycemia may beconsidered as inversely related the standard of care is that clinicalstudies aim at reducing A1C while reporting the observed rate ofhypoglycemia as the unavoidable evil that is part of the therapy.

While the foregoing solutions are beneficial in the management andtreatment of diabetes in some patients, or at least hold the promise ofbeing so, there continues to exist the need for methods, devices and/orsystems that would cost-effectively improve diabetes control in patientswherein a goal of diabetes management may be achieving glycemic balanceand/or improved glycemic composite index weighing both A1C and the riskfor or frequency of hypoglycemia. And the other needs and advantagesaddressed herein.

SUMMARY

Certain embodiments are directed to systems, devices and/or methods fortreating a patient's diabetes by providing treatment guidance. Forexample, a method for treating a patient's diabetes by providingtreatment guidance, the method comprising: storing one or morecomponents of the patient's insulin dosage regimen; obtaining datacorresponding to the patient's blood glucose-level measurementsdetermined at a plurality of times; tagging each of the bloodglucose-level measurements with an identifier reflective of when or whythe reading was obtained; and determining the patient's current glycemicstate relative to a desired balance point; and determining from at leastone of a plurality of the data corresponding to the patient's bloodglucose-level measurements whether and by how much to vary at least oneof the one or more components in the patient's present insulin dosageregimen to get closer to the patient's desired balance point; whereinthe desired balance point is the patient's lowest blood glucose-levelwithin a predetermined range achievable before increasing the frequencyof hypoglycemic events above a predetermined threshold.

Certain embodiments are directed to systems, devices and/or methods forupdating a patient's insulin dosage regimen. For example, the methodcomprising: storing one or more components of the patient's insulindosage regime; obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times;incrementing a timer based on at least one of the passage of apredetermined amount of time and the receipt of each blood glucose-levelmeasurement; tagging each of the blood glucose-level measurements withan identifier reflective of when the reading was obtained; determiningfor each of the obtained blood glucose-level measurements whether themeasurement reflects a hypoglycemic event or a severe hypoglycemicevent; and varying at least one of the one or more components in thepatient's insulin dosage regime in response to a determination that themost recent blood glucose-level measurement represents a severehypoglycemic event.

Certain embodiments are direct to apparatus for treating a patient'sdiabetes by providing treatment guidance. For example, an apparatuscomprising: a processor; and a computer readable medium coupled to theprocessor; wherein the combination of the processor and the computerreadable medium are configured to: store one or more components of thepatient's insulin dosage regimen; obtain data corresponding to thepatient's blood glucose-level measurements determined at a plurality oftimes; tag each of the blood glucose-level measurements with anidentifier reflective of when or why the reading was obtained; determinethe patient's current glycemic state relative to a desired balancepoint; and determine from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point; wherein the desired balance point isthe patient's lowest blood glucose-level within a predetermined rangeachievable before increasing the frequency of hypoglycemic events abovea predetermined threshold.

Certain embodiments are direct to apparatus for updating a patient'sinsulin dosage regimen. For example, an apparatus comprising: aprocessor; and a computer readable medium coupled to the processor;wherein the combination of the processor and the computer readablemedium are configured to: store one or more components of the patient'sinsulin dosage regime; obtain data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; incrementa timer based on at least one of the passage of a predetermined amountof time and the receipt of each blood glucose-level measurement; tageach of the blood glucose-level measurements with an identifierreflective of when the reading was obtained; determine for each of theobtained blood glucose-level measurements whether the measurementreflects a hypoglycemic event or a severe hypoglycemic event; vary atleast one of the one or more components in the patient's insulin dosageregime in response to a determination that the most recent bloodglucose-level measurement represents a severe hypoglycemic event.

Certain embodiments are direct to apparatus for improving the health ofa diabetic population. For example, an apparatus comprising: a processorand a computer readable medium coupled to the processor and collectivelycapable of: (a) storing one or more components of the patient's insulindosage regimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point; wherein the desired balance point isthe patient's lowest blood glucose-level within a predetermined rangeachievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Certain embodiments are directed to systems, methods and or devices forimproving the health of a diabetic population. For example, a methodcomprising: treating a least one diabetic patient in the populationusing a device capable of: (a) storing one or more components of thepatient's insulin dosage regimen; (b) obtaining data corresponding tothe patient's blood glucose-level measurements determined at a pluralityof times; (c) tagging each of the blood glucose-level measurements withan identifier reflective of when or why the reading was obtained; (d)determining the patient's current glycemic state relative to a desiredbalance point; and (e) determining from at least one of a plurality ofthe data corresponding to the patient's blood glucose-level measurementswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen to get closerto the patient's desired balance point; wherein the desired balancepoint is the patient's lowest blood glucose-level within a predeterminedrange achievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Certain embodiments are directed to systems, methods and or devices forimproving the health of a diabetic population. For example, a methodcomprising: identifying at least one diabetic patient; treating the aleast one diabetic patient to control the patient's blood glucose level;wherein the patient's blood glucose level is controlled using a devicecapable of: (a) storing one or more components of the patient's insulindosage regimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point; wherein the desired balance point isthe patient's lowest blood glucose-level within a predetermined rangeachievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Certain embodiments of the methods, devices and/or systems disclosedherein are useful to achieve reduction in the frequency of hypoglycemiaby changing the distribution of insulin between different administrationpoints rather than reducing the daily total insulin dosage. Certainembodiments are directed to methods, systems and/or devices for treatinga patient's diabetes by providing treatment guidance wherein thefrequency of hypoglycemic events is reduced without significantlyreducing the total amount of insulin used by the patient.

In certain embodiments, the system comprises at least a first memory forstoring data inputs corresponding at least to one or more components ofa patient's present insulin dosage regimen, and data inputscorresponding at least to the patient's blood-glucose-level measurementsdetermined at a plurality of times; and a processor operativelyconnected to the at least first memory. The processor is programmed atleast to determine from the data inputs corresponding to the patient'sblood-glucose-level measurements determined at a plurality of timeswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen.

In certain embodiments, the at least first memory and the processor areresident in a single apparatus. Per one feature, the single apparatusfurther comprises a glucose meter. The glucose meter may be separatefrom the single apparatus, further to which the glucose meter is adaptedto communicate to the at least first memory of the single apparatus thedata inputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times.

Per one feature thereof, the single apparatus may further comprises dataentry means for entering data inputs corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes directly into the at least first memory. In certain aspects, thesingle apparatus may further comprises a way to enter data inputscorresponding at least to the patient's blood-glucose-level measurementsdetermined at a plurality of times directly into the at least firstmemory.

There may, per other aspects of the disclosure, further be provided dataentry means disposed at a location remote from the single apparatus forremotely entering data inputs corresponding at least to the one or morecomponents in the patient's present insulin dosage regimen into the atleast first memory. In certain aspects, the data entry may be disposedat a location remote from the single apparatus for remotely enteringdata inputs corresponding at least to the one or more components in thepatient's present insulin dosage regimen into the at least first memory.

Certain embodiments may comprise at least a first data entry meansdisposed at a location remote from the at least first memory andprocessor for remotely entering data inputs corresponding at least tothe one or more components in the patient's present insulin dosageregimen into the at least first memory, and at least second data entrymeans, disposed at a location remote from the at least first memory,processor and at least first data entry means, for remotely enteringdata inputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times into the at least firstmemory.

Certain embodiments may comprise a way to enter a first data setdisposed at a location remote from the at least first memory andprocessor for remotely entering data inputs corresponding at least tothe one or more components in the patient's present insulin dosageregimen into the at least first memory, and a way to enter a second dataset, disposed at a location remote from the at least first memory,processor and the first data set corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of times thatis entered into the at least first memory.

In certain aspects, the data inputs corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes are each associated with an identifier indicative of when themeasurement was input into the memory. Optionally, there may be provideddata entry means enabling a user to define the identifier associatedwith each blood-glucose-level measurement data-input, to confirm thecorrectness of the identifier associated with each blood-glucose-levelmeasurement data-input, and/or to modify the identifier associated witheach blood-glucose-level measurement data-input. Optionally, there maybe provided a way to enter data enabling a user to define the identifierassociated with each blood-glucose-level measurement data-input, toconfirm the correctness of the identifier associated with eachblood-glucose-level measurement data-input, and/or to modify theidentifier associated with each blood-glucose-level measurementdata-input.

According to other embodiments, the processor is programmed to determineon a predefined schedule whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen.

In certain aspects, the processor is programmed to determine whethereach data input corresponding to the patient's blood-glucose-levelmeasurements represents a severe hypoglycemic event, and to vary atleast one of the one or more components in the patient's present insulindosage regimen in response to a determination that a data inputcorresponding to the patient's blood-glucose-level measurementsrepresents a severe hypoglycemic event.

According to certain embodiments, the processor is programmed todetermine from the data inputs corresponding to the patient'sblood-glucose-level measurements determined at a plurality of times ifthere have been an excessive number of hypoglycemic events over apredefined period of time, and to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen in responseto a determination that there have been an excessive number of suchhypoglycemic events over a predefined period of time.

In certain aspects, the processor is programmed to determine from thedata inputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times if the patient'sblood-glucose level measurements fall within or outside of a predefinedrange, and to vary at least one of the one or more components in thepatient's present insulin dosage regimen only if the patient'sblood-glucose level measurements fall outside of the predefined range.The processor may be further programmed to determine from the datainputs corresponding at least to the patient's blood-glucose-levelmeasurements determined at a plurality of times whether the patient'sblood-glucose-level measurements determined at a plurality of timesrepresent a normal or abnormal distribution. In certain aspects, thisdetermination comprises determining whether the third moment of thedistribution of the patient's blood-glucose-level measurementsdetermined at a plurality of times fall within a predefined range.

In certain embodiments, where the one or more components in thepatient's present insulin dosage regimen comprise a long-acting insulindosage component, the processor is programmed to determine from theidentifier indicative of when a measurement was input into the memory atleast whether the measurement is a morning or bed-timeblood-glucose-level measurement, to determine whether the patient'smorning and bed-time blood-glucose-level measurements fall within apredefined range, and to determine by how much to vary the patient'slong-acting insulin dosage component only when the patient's morning andbed-time blood-glucose-level measurements are determined to fall outsideof the said predefined range. In connection therewith, the processor mayfurther be programmed to factor in an insulin sensitivity correctionfactor that defines both the percentage by which any of the one or morecomponents of the insulin dosage regimen may be varied and the directionin which any fractional variations in any of the one or more componentsare rounded to the nearest whole number. Optionally, the at least firstmemory further stores data inputs corresponding to a patient's presentweight, and the insulin sensitivity correction factor is in partdetermined from the patient's present weight. Per certain aspects, thedetermination of by how much to vary the long-acting insulin dosagecomponent of a patient's present insulin dosage regimen may be afunction of the present long-acting insulin dosage, the insulinsensitivity correction factor, and the patient's blood-glucose-levelmeasurements.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen comprise a short-acting insulin dosagecomponent defined by a carbohydrate ratio and plasma glucose correctionfactor, and the processor is programmed to determine whether and by howmuch to vary the patient's carbohydrate ratio and plasma glucosecorrection factor. In connection with this determination, the processormay be programmed to factor in an insulin sensitivity correction factorthat defines both the percentage by which any one or more components ofthe insulin dosage regimen may be varied and the direction in which anyfractional variations in the one or more components are rounded to thenearest whole number.

In certain embodiments, the determination of by how much to vary thepresent plasma glucose correction factor component of a patient'sinsulin dosage regimen may be a function of a predefined value dividedby the mean of the total daily dosage of insulin administered to thepatient, the patient's present plasma glucose correction factor, and theinsulin sensitivity correction factor. Alternatively, a valuerepresenting twice the patient's daily dosage of long-acting insulin inthe present insulin dosage regimen may be substituted for the mean ofthe total daily dosage of insulin administered to the patient as anapproximation thereof. Per still another feature hereof, the plasmaglucose correction factor component of the patient's insulin dosageregimen may be quantized to predefined steps of mg/dL.

According to certain embodiments, the determination of by how much tovary the present carbohydrate ratio component of a patient's insulindosage regimen is a function of a predefined value divided by the meanof the total daily dosage of insulin administered to the patient, thepatient's present carbohydrate ratio, and the insulin sensitivitycorrection factor. Alternatively, a value representing twice thepatient's daily dosage of long-acting insulin in the present insulindosage regimen is substituted for the mean of the total daily dosage ofinsulin administered to the patient as an approximation thereof. Furtherhereto, the processor may also be programmed to determine a correctionfactor that allows variations to the carbohydrate ratio component of apatient's insulin dosage regimen to be altered in order to compensatefor a patient's individual response to insulin at different times of theday.

A further feature of certain embodiments is that the one or morecomponents in the patient's present insulin dosage regimen comprise along-acting insulin dosage component, and the determination of by howmuch to vary the long-acting insulin dosage component is constrained toan amount of variation within predefined limits.

In certain embodiments the one or more components in the patient'spresent insulin dosage regimen comprise a short-acting insulin dosagecomponent defined by a carbohydrate ratio and plasma glucose correctionfactor, and the determination of by how much to vary any one or more ofeach component in the short-acting insulin dosage is constrained to anamount of variation within predefined limits.

According to certain embodiments, the one or more components in thepatient's present insulin dosage regimen comprise a short-acting insulindosage component taken according to a sliding scale, and the processoris programmed to determine whether and by how much to vary at least oneof the components of the sliding scale. The determination of by how muchto vary the sliding scale may further be constrained to an amount ofvariation within predefined limits.

According to certain embodiments, the one or more components in thepatient's present insulin dosage regimen comprise a short-acting insulindosage component where meal bolus components, whether a carbohydrate toinsulin ratio or a fixed dose with a sliding scale, may differ from onemeal to the other, and the processor is programmed to determine whetherand by how much to vary at least one of the components independent ofthe other components. The determination of by how much to vary a dosagecomponent may further be constrained to an amount of variation withinpredefined limits.

According to certain embodiments, insulin dosage may comprise of asingle component representing a daily total of long acting insulin theuser has to administer. Such daily total may be administer as a singleinjection or split between more than one injection, and the processor isprogrammed to determine whether and by how much to vary the daily totalinsulin units of the long acting insulin component.

According to certain embodiments, insulin dosage may comprise of a twocomponent representing a two separate insulin doses to be taken withspecific events. Such example may be a breakfast dose and a dinner doseof premixed or biphasic insulin, and the processor is programmed todetermine whether and by how much to vary at least one of the twodifferent dosage component.

In certain embodiments, the processor is programmed to calculateglycemic index indicative of the user metabolic state associated with aparticular event. In certain embodiments, glycemic index is a singlenumber comprised of the average, median, minimum, maximum, or othermetrics of the data set being measured, and the processor is programmedto determine whether and by how much to vary at least one of the one ormore insulin dosage components based at least on glycemic index.

Certain embodiments are methods for determining the amount of insulinneeded by a diabetic comprising the steps of: A. taking a plurality ofhistorical blood glucose readings from a patient; B. taking a pluralityof historical readings of insulin administered to a patient; C.determining a protocol for providing insulin to a patient based upon theplurality of historical readings and a patient's blood glucose readingat a fixed time; and D. providing insulin to the patient based upon theprotocol, historical readings of Steps A and B and the patient's bloodglucose reading of Step C. In certain aspects, the protocol isreevaluated over a fixed time interval. In certain aspects, the fixedtime interval is, for example, weekly or every two weeks. In certainaspects, the protocol is reevaluated based on predefined events (e.g., ablood glucose reading indicating a hypo-glycemic event) in anasynchronous manner. In certain embodiments, the plurality of historicalreadings of insulin administered to a patient includes the number ofunits and the type of insulin for each time insulin is administered to apatient.

Certain embodiments are to systems to determine the amount of insulinneeded by a diabetic patient comprising: A. means to input blood glucosereadings of a patient; B. means to determine a protocol based upon theblood glucose readings; and C. means to modify the protocol over aperiod of time based upon historical blood glucose readings. In certainaspects, the system is provided within a glucose meter. In certainaspects, the system further comprises means to input quantities ofinsulin administered by a patient. In certain aspects, the systemfurther comprises an infusion pump to administer insulin to the patientbased upon the protocol and the blood glucose readings.

Certain embodiments are systems to determine the amount of insulinneeded by a diabetic patient comprising: A. a way to input blood glucosereadings of a patient; B. a way to determine a protocol based upon theblood glucose readings; and C. a way to modify the protocol over aperiod of time based upon historical blood glucose readings. In certainaspects, the system is provided within a glucose meter. In certainaspects, the system further comprises a way to input quantities ofinsulin administered by a patient. In certain aspects, the systemfurther comprises an infusion pump to administer insulin to the patientbased upon the protocol and the blood glucose readings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings and figures facilitate an understanding of thevarious embodiments of this technology.

FIG. 1 is a simplified schematic of an apparatus according to certainexemplary embodiments.

FIG. 2 is a drawing of a representative display for providinginformation to a patient.

FIG. 3 is a drawing of another representative display for providinginformation to a patient.

FIG. 4 is a drawing yet another representative display for providinginformation to a patient.

FIG. 5 is a drawing of still another representative display forproviding information to a patient.

FIG. 6 is a simplified diagram of an apparatus for employing thedisclosed system, according to certain embodiments thereof.

FIG. 7 is a simplified diagram of an apparatus for employing thedisclosed system, according to certain embodiments.

FIG. 8 is a simplified diagram of an apparatus for employing thedisclosed system, according to certain embodiments thereof.

FIG. 9 is a schematic view of an exemplary arrangement, according tocertain embodiments.

FIG. 10 is a schematic view of an exemplary arrangement for employing,according to certain embodiments.

FIG. 11 is a generalized diagram of the steps employed in updating apatient's insulin dosage regimen according to certain exemplaryembodiments.

FIG. 12 is a flowchart of an exemplary algorithm employed in updating apatient's insulin dosage regimen according to certain exemplaryembodiments

FIG. 13 illustrates a subject with low variability of glucose levels.

FIG. 14 illustrates a subject with high variability of glucose level.

FIG. 15 illustrates a patient with varying level of glycemicvariability.

FIG. 16 illustrates a subject with a high glucose level and lowvariability.

FIG. 17 illustrated insulin dosage of a subject with a high glucoselevel and low variability.

FIG. 18 illustrates a subject with low glucose with high variability.

FIG. 19 illustrates insulin dosage of a subject with low glucose withhigh variability.

FIG. 20 illustrates a subject with low glucose with low variability.

FIG. 21 illustrates insulin dosage of a subject with low glucose withlow variability.

FIG. 22 illustrates a subject with high glucose with high variability.

FIG. 23 illustrates insulin dosage of a subject with high glucose withhigh variability.

FIG. 24 illustrates blood glucose levels of a subject on a premixedinsulin therapy.

FIG. 25 illustrates insulin dosage of a subject on premixed insulintherapy.

FIG. 26 illustrates blood glucose levels of a subject on a premixedinsulin therapy.

FIG. 27 illustrates insulin dosage of a subject on premixed insulintherapy.

FIG. 28 illustrates blood glucose levels of a subject taking arelatively small daily total of ˜45 units per day almost equally dividedbetween basal and bolus.

FIG. 29 illustrates insulin dosage of a subject taking a relativelysmall daily total of ˜45 units per day almost equally divided betweenbasal and bolus.

FIG. 30 illustrates the weekly mean glucose (and regression line),cumulatively for all patients in this example, according to certainembodiments.

FIG. 31 illustrates the weekly mean glucose (and regression line),cumulatively in groups I and II in this example, according to certainembodiments.

FIG. 32 illustrates the weekly mean glucose in group III (due to lesserdata points, a regression line was not plotted) in this example,according to certain embodiments.

FIG. 33 illustrates the weekly mean glucose (and regression line) ofpatients with and without frequent hypoglycemia. During the active 12weeks weekly mean glucose improved when possible in this example,according to certain embodiments.

FIG. 34 illustrates the distribution of hypoglycemic glucose readingsduring the 12-week active phase and the 4-week run-in period in thisexample, according to certain embodiments.

FIG. 35 illustrates the frequency of minor hypoglycemia (glucose<65mg/dl) during each quartile for patients with or without frequenthypoglycemia (>85 events per patient-year) in this example, according tocertain embodiments.

FIG. 36 illustrates the total daily insulin in patients with differentfrequencies of minor hypoglycemia. During the active 12-week period, thefrequency and severity of hypoglycemia decreased in this example,according to certain embodiments.

DETAILED DESCRIPTION

The following description is provided in relation to several embodimentswhich may share common characteristics and features. It is to beunderstood that one or more features of any one embodiment may becombinable with one or more features of the other embodiments. Inaddition, any single feature or combination of features in any of theembodiments may constitute additional embodiments.

In this specification, the word “comprising” is to be understood in its“open” sense, that is, in the sense of “including”, and thus not limitedto its “closed” sense, that is the sense of “consisting only of”. Acorresponding meaning is to be attributed to the corresponding words“comprise”, “comprised” and “comprises” where they appear.

The subject headings used in the detailed description are included onlyfor the ease of reference of the reader and should not be used to limitthe subject matter found throughout the disclosure or the claims. Thesubject headings should not be used in construing the scope of theclaims or the claim limitations.

The term “insulin dosage function” or “IDF” as used herein with respectto certain embodiments refers to a lookup table indicative of an insulinregimen, a protocol, or a combination thereof that a user follows. Forexample, for a patient following premixed insulin regimen the insulindosage function may contain two numbers associated with two eventsreflective of two insulin injection per day, say X insulin units withbreakfast and Y insulin units with dinner. The term IDF history as usedherein with respect to certain embodiments refers to chronology ofinsulin dosage functions and external insulin dosage functions viewed asone data set. The first IDF in an IDF history is the active insulindosage function or the lookup table currently use to recommend the useran appropriate insulin dose per a particular event and event relatedinformation. The next record is the second IDF in IDF history thefollowing is the third IDF in IDF history and so forth through theexisting records in IDF history

The term “partial update” as used herein with respect to certainembodiments refers to the operation of updating a single dosagecomponent in an insulin dosage function. In certain embodiments, apartial update may change more than one dosage components. In certainembodiments, a partial update may not interfere with the synchronousdosage adjustment frequency. In certain embodiments, an event thatcaused a partial updated may be excluded when the time to perform asynchronous adjustment is due.

The term “full update” as used herein with respect to certainembodiments refers to the operation of assessing insulin dosagecomponents to determine if and by how much to change one or more of thedosage components. In certain embodiments, the operation of a fullupdate results in a reset of the synchronous clock. In certainembodiments, the operation of a full update may result in data expiringfrom the period under evaluation. For example, certain embodiments mayemploy a counter to determine the number of hypoglycemic events thatoccurred within a given interval, the process of a full update may causea reset of that counter.

The terms “severe hypoglycemic event” or “SHE” as used herein withrespect to certain embodiments refers to blood glucose value below acertain threshold. In certain embodiments, a severe hypoglycemic eventis a patient history event with glucose data less than 55 mg/dl. Incertain embodiments, a severe hypoglycemic event is a patient historyevent with glucose data less than 40, 45, 50, 55, 60, 65, 70 mg/dl orcombinations thereof.

Certain embodiments are directed to a therapeutic device which is aglucose meter equipped with artificial intelligence (AI) and capable ofoptimizing medication dosage of patients treated with various types ofinsulin, including optimizing combination of insulin types, i.e., bothshort and long acting insulin. Certain embodiments monitor patientglucose reading and additional parameters and modify insulin dosage asneeded in a similar manner to what an endocrinologist, or otherqualified health care provider, would do if that person had continuousaccess to patient's data. By dynamically modifying medication dosagebased on individual lifestyle and changing needs an optimal dosage levelis reached. In turn, this leads to superior glycemic control and betterpatient prognosis.

Glycemic Balance

A goal of diabetes management may be achieving glycemic balance and/orimproved glycemic composite index weighing both A1C and hypoglycemia.Another goal of diabetes management may be moving a patient towardsglycemic balance and/or improved glycemic composite index weighing atleast A1C and hypoglycemia. There are several potential ways ofminimizing a combination of two parameters sometime referred to asminimizing a cost function of two arguments. The definition of the costfunction is significant by itself as its shape may determine what typeof solution for the minimization problem exists. Convex cost functioncan be minimized by several methods and the minimal solution is unique.Optimization of convex function is well studied and books like “ConvexOptimization” by Boyd and Vandenberghe (Cambridge University Press,2004; ISBN-10: 0521833787) and others describes several known methods toperform such optimization. Unfortunately, widely acceptable definitionsof what is “an acceptable level of hypoglycemia” do not exist. However,certain aspects of the present disclosure are aimed at setting the goalof achieving a better glycemic balance or an improved glycemic compositeindex (GCI) by minimizing one argument while making an effort to keepthe other argument under a certain threshold. Certain aspects of thepresent disclosure are aimed at setting the goal of guiding a subject toa glycemic balance or an improved glycemic composite index (GCI) byminimizing one argument while making an effort to keep the otherargument under a certain threshold. For example, one possible approachis to minimize glycemic index (GI) as long as it is above a certainthreshold while keeping the frequency of hypoglycemia below anotherthreshold; wherein GI is a measure of a set reducing a plurality ofhistoric blood glucose level to a single variable. For example GI can bethe mean or median of a given set of glucose values. Other metrics canalso be combined like the minimum, maximum, minimum of the mean ormedian, and other combinations like pattern recognition capable ofreducing a multidimensional data set to a single value. In certainembodiments, it may be desired to increase one or more of the insulindosage component in order to reduce glycemic index assuming glycemicindex is above 120 mg/dl and provided that there have been no more than3 low blood glucose values during the period under observation. Othernumbers are also contemplated like increasing one or more of the insulindosage components if glycemic index is above 150, 140, 130, 110, 100,90, or 80 mg/dl and provided that there were no more than 1, 2, 4, 5, 6,7, 8, 9, or 10 low blood glucose values during the period underobservation. In certain embodiments low blood glucose values may bedefine as glucose level below 80, 75, 70, 65, 60, 55, 50, or 45 mg/dl.Other methods as disclosed herein may also be chosen.

In certain embodiments, as typically done in constrained optimization adual approach is to reduce the frequency of hypoglycemia as long asglycemic index is below a certain threshold. For example, in certainembodiments, it may be desired to reduce on or more of the insulindosage components, if the frequency of hypoglycemia is more than 3during the observed interval and provided that glycemic index is lessthan 200 mg/dl. Other values like 1, 2, 4, 5, 6, 7, or 8 hypoglycemicepisodes may be combined with glycemic a index less than 250, 240, 230,220, 210, 190, 180, 170, 160, 150, 140, 130, 120 can also be used.

For example, to improve GCI certain embodiments may chose to reduce meanglucose as long as the rate of hypoglycemia does not exceed a certainthreshold. If insulin is used to reduce mean glucose then increasedinsulin dosage may result in decreased glucose level. The reduction inglucose may, in some cases, lead to hypoglycemia. If the rate ofhypoglycemia exceeds a predefined threshold then insulin dosage may bedecreased. Decreased insulin dosage may lead to increased averageglucose, which in turn reduces the chances of experiencing hypoglycemia.An exemplary algorithm that achieve that can be described as follows:

count the number of hypoglycemic episodes over a given time to determinehypoglycemia rate (HR)

if HR > N reduce insulin level    otherwise       calculate glycemicindex (GI)       if GI < A₁          decrease insulin dosage       ifGI > A₂          increase insulin dosage.

The unacceptable hypoglycemic rate threshold (N) may be set to 80events/year, although other numbers such as 50, 60, 70, 75, 85, 90, 100,110 or 120 events/year may also be used. The two other thresholds A₁ andA₂ may be selected to drive GI to a desired target. For example one canset a lower level of 80 mg/dl and a higher level of 130 mg/dl, althoughother combinations of numbers may also be used. For example, glucosevalues of 60, 65, 70, 75, 85, 90, 95, 100, 105 or 110 mg/dl can be usedas the lower threshold A₁, and glucose values of 110, 115, 120, 125,135, 140, 145, 150, 155 or 160 mg/dl can be used as the upper thresholdA₂.

The glycemic index (GI) is various statistics that may be derived fromavailable glucose data. For example, statistics that may be used aremean, median, min, max, other mathematical operator that can be extractfrom a particular set of glucose data (e.g. pattern detection), orcombinations thereof.

Hypoglycemic Events

To correctly account for hypoglycemic events for the purpose of insulinadjustment it is useful that such events would be appropriately tagged.In general, a non-fasting glucose level is reflective of the previousinsulin injection. For example, a lunch glucose reading is reflective ofthe effect that the breakfast insulin bolus may had on the user bloodglucose levels. In some cases, as a non-limiting example, when a userfeels symptoms of hypoglycemia, they may measure glucose outside oftheir regular schedule. Such glucose data point is typically marked as‘Other’. If the ‘Other’ glucose level is low it is useful to identifythe insulin injection that most likely caused the low ‘Other’ bloodglucose level so that the appropriate insulin dosage component would bereduced accordingly. The process of reclassifying an ‘Other’ eventrelies on the timestamp of the ‘Other’ event and the time that past froma previous event that was not classified as ‘Other’, for example a mealevent. The pharmacokinetic profile of the particular insulin used by theuser can help set a time window during which an injection may had aparticular effect that resulted in a low blood glucose level. In oneexample, the user may be administering fast-acting insulin which may beactive 30 minutes post injection and its effect will completely wear offwithin 6 hours post injection. For this example, if a ‘Lunch’ event isrecorded at 12 PM and a low blood glucose level is recorded as ‘Other’at 12:10 PM it is unlikely that this low blood glucose level is a resultof the Lunch fast-acting insulin injection because it happened tooquickly and fast-acting insulin takes longer to start affecting bloodglucose levels. Similarly, if an ‘Other’ event is recorded after 6 PM itis unlikely the cause of the lunch fast-acting injection since itseffect has already worn off the user. Therefore, it is understood thatfor a fast acting insulin, with a pharmacokinetic activity profile of 30minutes to 6 hours from an injection, low blood glucose levels, taggedas ‘Other’, that occurs within 30 minutes to 6 hours from an injectionevent are likely a result of that injection. Accordingly, it may bedesired to reduce the dosage component that most likely caused that lowblood glucose level.

Other types of insulin may also be considered. Some rapid-acting orultra rapid-acting insulin may have a pharmacokinetic activity profilewhere they start affecting blood glucose levels within 15 minutes fromadministration and their effect wears off within 3 hours. Older types ofinsulin, like Regular Insulin (e.g. by Elly Lilly), may take 45 minutesto start affecting blood glucose levels and as many as 8 hours to wearoff. If a user administers pre-mixed or biphasic insulin then thepharmacokinetic profile of such drugs, e.g. Humulin 70/30, Novolin70/30, Humalog Mix 75/25, or Novolog Mix 70/30, may be affecting bloodglucose levels starting about 1 hour after injection and ending around12 hours post injection. It is also appreciated that long-actinginsulin's, such as Lantus® or Levemir®, have a fairly flatpharmacokinetic profile and their activity level is nearly constant overa 24 hours period. Therefore, if a user is administering a combinationof long acting and fast acting insulin for their diabetes managementglucose levels tagged as ‘Other’ that falls outside of a particular timewindow following a fast-acting insulin administration are most likelyattributed to the background long-acting insulin injection. Accordingly,if an ‘Other’ event recorded a low glucose level and appeared 7 hourspost the last meal event recorded in history, that glucose level can beused to reduce the long-acting insulin dosage component.

In certain embodiments, it may be desired to reduce an insulin dosagecomponent as soon as a very low blood glucose (VLG) level has beenlogged into history. A VLG level may be define as blood glucose levelbelow 60 mg/dl, although other numbers, such as below 70, 65, 55, 50,45, 40 or 35 mg/dl as well as other numbers in similar ranges can alsobe used. Once a VLG has been logged it is desired that the dosagecomponent that has most likely caused that VLG will be reduced. Thatdosage component can be proportionally reduced by 10%, 20%, 30%, 40% or50%, or reduced by a fixed number of insulin units such as reduced by 1,2, 3, 4, 6, 8, 10, or other reasonable numbers in that range. Thereduction of dosage may also be a combination of the two, for exampledosage component will be reduced by the greater of (X units or Y %). Inthis case, if a user is administering 10 insulin units say X=2 and Y=10%than the greater of 2 [insulin units] or 10% of 10 [insulin units] is 2[insulin units], in which case the new dosage component will be 8insulin units, instead of the previous component that was 10. In othercases the combination may be the smaller of (X units or Y %). If thelatter was applied to the previous example than the smaller of the twois 1 insulin units and the new recommended dosage component would be 9insulin units, instead of the previous component that was 10. Anotheralternative to a dosage component reduction is a ‘roll back’, that isfind the previous dosage component that is lower than the current oneand replace the current component with the previous one. For example, ifan insulin dosage component was 10 units and was later increased to 13units. And, a while later the dosage component of 13 is suspected as thereason behind a VLG it may be desired to replace the component 13 withthe previous lower value of 10. This is done regardless ofproportionality or a fixed minimal/maximal reduction because accordingto the data in the device history the previous value of 10 did not causeany VLG.

In some cases it would be appreciated that glucose values may be low butnot very low. A range for low glucose LG can be defined as values thatare not VLG, contemplated before, yet lower than a particular value like75 mg/dl. Other numbers can also be used for the upper threshold like,90, 85, 80, 70, 65, 60, 55, 50, 45 or 40 mg/dl or other reasonablenumbers in that range. In some embodiments, it may be desired to accountfor a plurality of LG values even if independently none of them accountsas a VLG to updated insulin dosage components. This is particularlyuseful if a similar event is suspicious as causing the LG values. Forexample if a dosage was installed on Monday evening and Tuesday lunch LGvalue is recorded and Wednesday lunch LG value is recorded it may bedesired to reduce the breakfast dosage component. Another example can bethat 3 LG values have been recorded for different events within a 24hours period. Yet another example can be that 4 LG values have beenrecorded for different events from the time last dosage was instated.Other combinations, like 3 or more LG values for a particular event, 2LG values or more within a 24 hours period, or 2, 3, 4, 5, 6, 7, 8, 9,10 LG values recorded from the time stamp when current dosage wasinstalled, are also contemplated.

It is appreciated that low blood glucose value are typically anindication the user is administering too much insulin for its currentmetabolic state. This condition may lead to VLG or to a severehypoglycemic event that is potentially life threatening. It is thereforedesired that a system adjusting insulin dosage is capable of reducingone or more of the user's insulin dosage components in an attempt toprevent the situation from having a negative clinical outcomes. Theaforementioned behavior of a system that adjusts insulin dosage on asynchronous basis, e.g., once a week, can be summarized as follows: ifone or more VLG values have been logged by the system an effort is madeto identify a dosage component that may have caused that one or more VLGvalues and reduced it according to dosage reduction rules. This responseto an occurrence of one or more VLG values may or may not reset thesynchronous time base for the insulin adjustment process. If one or moreLG values have been logged by the system there is an attempt to assessthe cause of the one or more LG values and to respond accordingly byreducing one or more of the insulin dosage components. Such a reductionmay or may not lead to a reset of the synchronous clock.

When adjusting insulin dosage it may be desirable to preventoscillations, i.e., low blood glucose levels leading to a dosagereduction leading to higher blood glucose levels leading to a dosageincrease leading to lower blood glucose levels and so forth. Severalmechanisms can be used to dampen, reduce or substantially decrease,unstable dosage oscillations. One such mechanism would be inserting ‘offintervals’ between different directions of dosage adjustments. Forexample a system may follow a rule that if a dosage was reduced frombaseline then the next dosage adjustment step can be further reductionor keep in place but not an increase. This way a dosage increase willtypically never follow a dosage decrease reducing the likelihood ofblood glucose levels oscillations. Another mechanism can be that if adosage reduction occurred and an increase is recommended in thefollowing dosage adjustment step, then such increase should typically belimited. The increase can be limited to be less than a particular level,for example, less than the value that was used before the reductionoccurred, or no greater than the level that was used before thereduction occurred, or not to exceed that level that was used before thereduction occurred by more than 5%, 10%, 15% or 20%, or 2 insulin unitsor 4 insulin units, or other similar expression. This way it isunderstood that the insulin dosage adjustment system is using short termmemory by not only reviewing the blood glucose data accumulated inhistory during the period under review but also utilizing the dosagehistory that preceded the period under review.

It would be appreciated that a similar mechanism can be used for theother direction, i.e., a dosage decrease that followed a prior increase.Such decrease may also be limited, dampened, reduced, to prevent, orsubstantially prevent unstable oscillations. However, it is understoodthat insulin dosage reduction is typically done to improve the safety ofthe therapy and prevent future hypoglycemia.

In some embodiments it is desired to prevent consecutive dosageincrements as the user response to such increments may be delayed. Adelayed response to insulin dosage increments may result in severehypoglycemia or other adverse events. In some embodiments thecontemplated system may use a consecutive dosage increment rule toestablish an ‘off period’ if an excessive number of consecutive dosageincrements occurred. For example, a system may employ a rule thatprevents 2 consecutive increments from occurring, i.e., creating an ‘offperiod’ after each dosage increment allowing for a delayed response.Other rules may also apply, for example, allowing for two consecutiveincrements in dosage but not 3, or allowing for 3 consecutive dosageincrements but not 4, or allowing for 4 consecutive increments but notfive.

In certain embodiments, there are several ways one can use to assess asto whether the new dosage represents an increment compared to theprevious dosage. Some examples are give below in table 1. This can besimple for basal only insulin regimen where the user has to administerZ₁ insulin units per day. Then, if the new dosage components Z₂ isgreater than Z₁ the dosage has been increased. However, for more complexregimens such as premixed/biphasic insulin therapy or basal bolusinsulin therapy alternative definitions can be used to define whatconstitutes a dosage increase. For example, for a premixed/biphasicinsulin regimen each dosage component is simply the dose one needs toadminister for a given event. That is, a premixed insulin dosage mayinclude a dose of X₁ units of insulin at breakfast and a dose of Y₁units at dinner. If the new dosage includes X₂ and Y₂ then severalmethods can be used to determine an increment, e.g., the methods shownin Table 1 below:

TABLE 1 X₂ + Y₂ > X₁ + Y₁ X₂ > X₁ and + Y₂ ≧ Y₁ Y₂ > Y₁ and + X₂ ≧ X₁X₂ > X₁ and + Y₂ < Y₁ but X₂ + Y₂ > X₁ + Y₁ Y₂ > Y₁ and + X₂ < X₁ butX₂ + Y₂ > X₁ + Y₁ either X₂ > X₁ or Y₂ > Y₁

Regardless of the definition used, it may be desirable to preventsuccessive insulin dosage increments. In some embodiments, a projecteddaily total such as the sum of the dosage components may be used todetermine whether the current insulin dosage represents an increasecompared to prior week. In regimens that require carbohydrate counting,the projected daily total would require estimating average meal size asthe dosage component are ratios and cannot be simply added to determinedaily total. Meal size estimates can relies on recorded carbohydrateintake logged in the system over a predefined period of time, forexample, the last week, the last couple of weeks, or the last month. Anestimated meal content can be calculated for different meals or as adaily total recorded carbohydrate intake.

In some embodiments, historic data stored in the system memory may beincomplete. In certain embodiments, incomplete data may be defined asless than 3 data points for a particular events. In other embodiments,incomplete data may be defined as less than 5, 4, 2, or 1 data pointsper event. In some instances, the user may chose to only measure fastingblood glucose. This has a varying level of meaning depending on theinsulin regimen used by the user. If a user is following a basal onlyregimen than fasting blood glucose level may be sufficient to safely andeffectively adjust insulin dosage to achieve a better glycemic balance.However, for a person using premixed insulin therapy, that administersinsulin twice a day, a single test per day may not suffice toappropriately adjust insulin. In certain embodiments, the instance whena particular data set, e.g. events of type ‘Breakfast’, is in completeis referred to as a missing data set.

In certain embodiments, if a single data set is missing the system maydecide to keep insulin dosage unchanged for the particular period underobservation. If more than one data set is missing, e.g., breakfast andlunch data sets, it may be decided to keep insulin dosage unchanged. Inother embodiments, the presence of a missing data set may be a reason tolimit the allowed change for other dosage components. For example, for auser taking premixed insulin twice daily at breakfast and dinner it maybe decided that if breakfast data is missing than the breakfast dosagecomponent, that is adjusted using dinner data set, cannot be increasedto a level that is more than twice the dinner dosage component. Otherlimits are also contemplated. For example the breakfast dosage componentcannot be increased to more than 150% of the dinner component.

In certain embodiments used with basal-bolus insulin therapy, thepresence of a missing data set may be used to limit the ratio betweenfast acting and long acting insulin. For example, in the case of amissing data set it may be desired to keep the long acting insulindosage component no more than 70% of the total daily amount of insulininjected. In the case of a missing data set it may be desired to keepthe total fast insulin dosage components no more than 70% of the totaldaily amount of insulin injected. In other embodiments, it may bedesired to limit the increase allowed for a single fast acting insulindosage component. For example, if lunch data set is missing than thedinner dosage component can only be increased if the dinner dosagecomponent is no more than 40% of the total fast acting insulin dosage.Similar examples that relates to the other dosage components are alsocontemplated. For example, if dinner data is missing than the breakfastdosage component can only be increased if it is no more than 40% of thetotal fast acting insulin.

Balance Point

Clinical data may suggest that the optimal balance point is differentfor different people with diabetes. For example, in some studies it wasnoted that a small portion of the study population (about 10% ofsubjects) experienced about 90% of the severe hypoglycemic episodes. Itis very likely that some people with diabetes are more prone tohypoglycemia. For such individuals the optimal glycemic balance may meanA1C>7%, since A1C of 7% or less will place them at a too greater risk.

The optimal glycemic balance for each individual may vary overtime andthat there may be no ‘steady state’. That is, the optimal GCI for eachindividual may need to be constantly evaluated. One reason for this maybe that GCI may be affected by the variability of an individual glucosedata. For some that variability is low as illustrated in FIG. 13. FIG.13 illustrates a patient with low variability of glucose levels. Eachpoint of the figure represents weekly mean glucose data and the verticalbars are plus or minus one standard deviation. In others the variabilitymay be high as illustrated in FIG. 14. FIG. 14 illustrates a patientwith high variability of glucose level. Each point of the figurerepresents weekly mean glucose data and the vertical bars are plus orminus one standard deviation. In others the variability may beinconsistent as illustrated in FIG. 15. FIG. 15 illustrates a patientwith varying level of glycemic variability. Each point of the figurerepresents weekly mean glucose data and the vertical bars are plus orminus one standard deviation. In weeks 10-16 there are far more glucosevalues <65 mg/dl (the numbers beneath each bar) as compared to weeks 1-9despite the fact that mean glucose is roughly stable and in factslightly higher during the second period.

Overall, in certain embodiments applying a mass policy of optimizing GCImay be much safer than applying a policy aimed at reducing A1C.Neglecting to set therapy goals that accounts for hypoglycemia may leadto severe consequences and even death. However, by applying a policy ofoptimizing glycemic balanced or GCI, as illustrated in certainembodiments, one can be reassured that therapy will be intensify only aslong as it does not lead to too many hypoglycemic episodes.

There is little consensus as to what constitute minor hypoglycemia. Itis generally accepted that severe hypoglycemia is one that requires theassistant of a third party to be resolved. It is also accepted thatminor hypoglycemia may be the best predictor for severe hypoglycemia.However, while some define minor hypoglycemia as capillary glucoselevels below 70 mg/dl numbers such as 65, 50, and even 40 mg/dl can alsobe found.

For example, a subject with a high glucose level and low variability isillustrated in FIGS. 16 and 17. In this example, the subject requiresmore insulin. There are no hypoglycemic episodes and his A1C is 8.5% atweek 4 and 6.1% at week 16. FIG. 16 shows that throughout the 16 weeksperiod the patient had just one glucose level <65 mg/dl (week 15). FIG.17 shows that the total daily insulin more than double over 12 weeks(120 to 270 [IU]). More specifically, dinner and basal insulin more thandoubled (from 30→71 and 60→133, respectively), while breakfast and lunchalmost doubled (15 to 29 and 27, respectively). This particular subjectcan safely maintain A1C level of 6.1%, well below the recommended goalof 7%.

Another example of a subject with low glucose with high variability isillustrated in FIGS. 18 and 19. The subject had a baseline A1C of 8.5%yet during weeks 1 through 4, the ran-in period, the subject's meanglucose is below 150 mg/dl with 3 hypoglycemic episodes/week and week 4A1C is 7.2%. For this subject it is unsafe to keep A1C that low. Hence,improved glycemic composite index translates to higher mean glucose withless hypoglycemia. The subject had week 16 A1C of 7.7% but hypoglycemiarate decreased 3 folds. FIG. 18 illustrates that during the first 4weeks (ran-in period) subject has 3 hypoglycemic episodes per week (arate of 156 episodes/year). During the last 4 weeks the subject had 4hypoglycemic episodes, a rate of 52/yr. FIG. 19 illustrates that thatinsulin did not decrease but rather increased throughout theintervention period. Initially (weeks 5-6) it remains steady, then thepatient experienced a shift in its metabolic state to higher meanglucose. Therefore, insulin dosage slowly increases from ˜80 units a dayto nearly 140 units a day. During week 10, 3 hypoglycemic episodescaused a temporal reduction of dosage.

Another example of a subject with low glucose with low variability isillustrated in FIGS. 20 and 21. Here the subject had week 4 A1C of 7.7%,mean glucose is below 150 mg/dl, yet there are 6 episodes ofhypoglycemia during the run-in period. FIG. 20 illustrates that thesubject had low glycemic variability but also low mean glucose.Therefore, optimizing GCI is a delicate task balancing near normalglucose levels while keeping the rate of hypoglycemia at bay. Throughoutthe 12 weeks of intervention mean glucose is maintained near normalwhile annual rate of hypoglycemia decreases form 78 to 56. FIG. 21 showsthat for this subject the total daily insulin remains fairly stable at˜180 units, yet its distribution is shifted from being 45%/55% basal tobolus in week 4 to being 30%/70% basal to bolus in week 16.

Another example of a subject with high glucose with high variability isillustrated in FIGS. 22 and 23. Subject has week 4 A1C of 8.2%, meanglucose ˜180 mg/dl, yet 14 episodes of hypoglycemia (a rate of182/year). Subject requires significant reduction in insulin before itcan be increased again slowly. During last 3 weeks of the study thesubject is taking almost the same amount of insulin as during the run inperiod yet with minimal hypoglycemia. FIG. 22 illustrates a subject withhigh glucose, high glycemic variability, and high rate of hypoglycemiaduring the run in period. For this subject hypoglycemia rate decreasedfrom 182/yr to 48/yr, while A1C increased from 8.2% (week 4) to 9.7%(week 16). FIG. 23 as opposed to the previous 3 examples, illustratesthat this subject counts carbs to figure out his bolus doses. Hence,meal dosage is given as ratio. For example, dinner dosage starts at aratio of 1 insulin unit to every 15 grams of carbohydrates and end at aratio of 1 [IU]:9 [gr. carbs]. To reduce hypoglycemia basal dose isreduced from 25 [IU] to 12 [IU] (weeks 4 to 8). Thereafter, withouthypoglycemia insulin dosage is slowly increased. Eventually, the subjectis taking at the end of the study almost the same amount of insulin asin the beginning yet with a far different distribution.

In another example of a patient on premixed insulin therapy. FIG. 24shows a reduction in weekly mean glucose from weeks 1-4 ‘for no apparentreason’ as insulin dosage remained unchanged during that time. FIG. 24also shows that the increase in insulin dosage in weeks 5-8 (from adaily total of 92 units to 140 units) resulted in a significant increasein mean glucose (from ˜230 mg/dl to ˜300 mg/dl). Furthermore, in weeks12-14 mean glucose roughly equals that of week 5 although insulin dosageis ˜190 units a day (more than twice that of week 5). This patient hadA1C of 13.2% in week 0, 11.3% in week 4, and 9.1% in week 16. FIG. 25illustrates the fact that certain disclosed embodiments did not increasedosage for more than 4 consecutive weeks. Note that there is no dosageincrease in weeks 9 and 13 despite elevated glucose levels.

FIG. 26 and FIG. 27 illustrate that certain disclosed embodiments hadthe ability to adjust different dosage component independently for apatient on premixed therapy. In weeks 6-9 and 12-16 the patient isexperiencing some hypoglycemia throughout the day to which theembodiments respond by reducing the breakfast dosage component while thedinner component may still increase. This patient had week 0 A1C of9.1%, week 4 of 8%, and week 16 of 5.8%.

FIG. 28 illustrates a patient taking a relatively small daily total of˜45 units per day almost equally divided between basal and bolus. Thepatient mean glucose during the run-in period is almost at targethovering just below 150 mg/dl, yet A1C is 9% in week 0 and 8.4% in week4. Certain embodiments are capable of further improving glycemic balanceby slowly increasing the independent bolus dosage components to a finaldaily total of ˜57 units/day. Week 16 A1C is improved to 7.4% with onlytwo hypoglycemic episodes one in each of the final two weeks. FIG. 29illustrates that basal insulin starts at 24 and ends at 25 units/daywith a peak of 28 units/day for weeks 14-15. At the same time: breakfastdosage goes from 6 to 9 (+50%), lunch dosage goes from 6 to 11 (+83%),and dinner dosage goes from 8 to 11 (+37%). While the bolus dosageincrease may seem dramatic it was achieved in a safe manner withacceptable rate of hypoglycemia and well improved A1C.

These examples illustrate that in certain embodiments the goal ofdiabetes management may be achieving glycemic balance or improvedglycemic composite index weighing both A1C and hypoglycemia.

Certain embodiments of the present disclosure are directed to systems,methods and/or devices for treating a patient's diabetes by providingtreatment guidance based whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen to get closer to the patient's desired glycemic balance point.

Certain embodiments of the present disclosure are directed to systems,methods and/or devices for treating a patient's diabetes by providingtreatment guidance based whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen to get closer to the patient's desired glycemic balance pointand individual time-varying treatment targets.

Certain embodiments of the present disclosure are directed to systems,methods and/or devices for treating a patient's diabetes by providingtreatment guidance that are designed to slowly and/or safely guide itsuser to a better glycemic balance.

Certain embodiments are directed to providing guidance on a dynamicbasis for each individual subject in order to move the subject anappropriate glycemic balance.

In certain embodiments, treatment of a patients diabetes by providingtreatment guidance using glycemic balance may assumed one or more of thefollowing:

-   -   a) lowering mean glucose increase the chances of experiencing        hypoglycemia;    -   b) hypoglycemia poses a potential risk for the patients and        under certain conditions it should lead to an immediate dosage        adjustment (regardless of the synchronic interval);    -   c) a single severe hypoglycemic event may be an outlier. As        such, it requires an immediate attention but does not reset the        synchronous clock;    -   d) events may not need to be double counted, in other words, if        a dosage component was adjusted in response to (c) that        particular severe hypoglycemic data point will typically be        ignored and not used again when the synchronic evaluation of the        data occurs; and/or    -   e) dosage evaluation should typically reflect the current        dosage. That is, when an asynchronous, full, dosage adjustment        occurs (due to an excessive number of hypoglycemic events over        since the last full update) the synchronous clock would be reset        and the hypoglycemic events that caused the asynchronous full        dosage adjustment expire.

The result is that certain embodiments use a varying length window thatcontains the events that occurred after the last update but are no olderthan 7 days (this is done to allow events to expire based on time in thecase that there were not enough events recorded in history to adjustdosage). Other time periods may also be used such as 2, 3, 4, 5, 6, 8,9, 10 11, 12, 13 or 14 days. Certain embodiments may perform at leasttwo types of updates: partial and full.

A partial update may be triggered by a severe hypoglycemic event andimmediately adjusts (reduces) the dosage component that presumablycaused the severe low. It does not have to reset the clock and may betreated as an outlier until there is more evidence that it wasn't anoutlier (i.e, there are more hypoglycemic episodes). In certainembodiments, partial updates are only triggered by severe hypoglycemicepisodes. In certain embodiments any low blood glucose value, lowmeaning below a particular threshold, can lead to either a partial or afull update of the insulin dosage. In certain embodiments two or morelow blood glucose levels can lead to a full update. In certainembodiments one severe hypoglycemic episodes and two low blood glucoselevels may lead to a full update. In other embodiments, low bloodglucose values may only lead to partial updates. In certain embodiments,two or more low blood glucose value per event may lead to a full update.In other embodiments more than 3, 4, or 5 low blood glucose values mayresult in a full update.

A full update uses the available data in the valid history (for example,newer than the last dosage update and not older than 7 days) to adjustone or more dosage components. In certain embodiments, the full updatewill adjust all dosage components. In other embodiments, the full updatewill adjust one or more dosage components. Other time periods may alsobe used such as 2, 3, 4, 5, 6, 8, 9, 10 11, 12, 13 or 14 days. It isassumed that this data set reflects the up-to-date efficacy of theactive dosage. In certain embodiments, a full update resets thesynchronous clock thus causing the events that were part of this dosageadjustment to expire by becoming older than the most recent dosagetimestamp. In certain embodiments, a full update resets the synchronousclock thus causing a substantial portion of the events that were part ofthis dosage adjustment to expire by becoming older than the most recentdosage timestamp. In certain embodiments, a full update resets thesynchronous clock thus causing all of the events that were part of thisdosage adjustment to expire by becoming older than the most recentdosage timestamp. In certain embodiments, a full update resets thesynchronous clock thus causing a portion of the events that were part ofthis dosage adjustment to expire by becoming older than the most recentdosage timestamp.

In certain embodiments, full update may be triggered by time, by thedetermination that frequency of hypoglycemia exceeded certain limits, orcombinations thereof. A full update can also be triggered by externalinterventions, such as by a treating clinician or by incorporatingadditional knowledge that may affect the user metabolic state. Suchknowledge may be that the user has started or discontinued other drugs,or the development of physiological conditions weather temporal sicknesslike the flu or conditions like end stage renal failure. Other examplesthat can trigger a full update are a visit to the emergency room, anysort of trauma injury, or other medical conditions that would leadsomeone knowledgeable in this field to reset insulin dosage or regimenor both.

In certain embodiments, a full update may be triggered by time, forexample, more than 7 days have passed since last update. In such caseseach data set is evaluated for completeness. Certain embodiments requireat least 3 data points per event. If a certain event has less than 3data points it is declared as missing data. If data is missing from oneevent then certain safety measures are applied to make sure that theremaining dosage component are not going to change too aggressively. Forexample, If more than 1 data set is missing then it may be decided notto adjust dosage.

In certain embodiments, full update can also be triggered by thedetermination that frequency of hypoglycemia exceeded certain limits. Insuch cases it is highly likely that there is less than 3 data points perevent. Nonetheless, since the full update was triggered for safetycertain embodiments use whatever data is available in memory.

The logic behind certain embodiments is that a) you have to let a dosagesettle in; and, b) if a full update occurred than the events (includinglow) have to expire otherwise certain embodiments would be accountingfor events that do not reflect the efficacy of the current dosage (i.e.,hypoglycemic episodes that occurred before the active dosage wasinstated).

In certain embodiments one or more of the following may be combined:

-   -   1) Increasing insulin dosage may be done at a more gradual pace.        For example, certain embodiments may not allow more than 3, 4,        5, or 6 consecutive increases to insulin dosage. This results in        slower increases of dosage which may have longer terms effects:        for example if a subject starts with 50 units a day and mean        glucose levels in the 200s their dosage can increase 20%˜25% for        several weeks leading them to a daily total of about 100 units        in 4 weeks. While each change was small the cumulative effect        may take time to settle in. As can be seen in the subject        illustrated in FIGS. 16 and 17 mean glucose is coming down        significantly in weeks 9 and 13 although insulin dosage is        unchanged from previous week.    -   2) Hypoglycemia is an inherent part of insulin therapy. There is        no need to respond to it either aggressively or conservatively        unless it reflects on the active dosage.    -   3) Limited correlation between events. Certain embodiments treat        each event set independently. Correlation between events in some        embodiments is only considered when data is missing.    -   4) Certain embodiments make an attempt to prevent unstable        oscillations by limiting an increase that followed a decrease        not to exceed the level that caused the previous decrease.

In certain embodiments, glycemic index (GI) can be defined as theminimum of the average and the median of a particular data set, e.g.,historic blood glucose level tagged as ‘Lunch’ during the period underevaluation. For a regimen of basal-bolus insulin therapy, GI can then beused to adjust the breakfast dosage component in AIDF according to thefollowing table 2 where Δ is a number of insulin units to be added tothe current breakfast dosage component:

TABLE 2 GI Δ fixed meal bolus Δ for carbohydrate counting  0-50 −MAX(1,INT_MIN[0.1*BD(k), MAX(1, INT_MIN[(0.1*BD(k), 0.2*BD(k)]) 0.2*BD(k)])51-80 −MAX(1, INT_MIN[0.05*BD(k) MAX(1, INT_MIN[(0.05*BD(k), 0.1*BD(k)])0.1*BD(k)])  81-135 (0) (0) 136-200 MAX(1, INT_MIN[0.05*BD(k) −MAX(1,INT_MIN[(0.05*BD(k), 0.1*BD(k)]) 0.1*BD(k)]) 201-250 MAX(1,INT_MIN[0.1*BD(k), −MAX(1, INT_MIN[(0,1*BD(k), 0.2*BD(k)]) 0.2*BD(k)])251-300 MAX(1, INT_MIN[0.15*BD(k), −MAX(1, INT_MIN[0.15*BD(k),0.25*BD(k)]) 0.25*BD(k)]) 301+ MAX(1, INT_MIN[0.2*BD(k), −MAX(1,INT_MIN[0.2*BD(k), 0.3*BD(k)]) 0.3*BD(k)])Wherein for certain embodiments MAX is the maximum of; INT_MIN is theminimal integer within a given range; and BD(k) refers to the breakfastdosage component within the active IDF. Other ranges of can also be usedon the column in the left hand side. For example, GI ranges can be 0-60,61-70, 71-120, 121-180, 181-230, 231-280, and above 281. Other examplesare also valid. It would be understood that if a patient is using afixed breakfast bolus dose of 10 units and GI=140 than the new breakfastdosage component is adjusted to be 11 units. At the same time, if thepatient is using a carbohydrate to insulin ratio for breakfast of 1insulin units to 10 grams of carbohydrates then according to theright-hand column the new dosage component would be a ratio of 1 [IU]:9[grams of carbohydrates].

In certain embodiments different tables can be used to adjust differentdosage components. For example while breakfast dosage component may beadjusted according to the example given in above, certain embodimentsmay use the following table 3 to adjust the dinner dosage component.

TABLE 3 GI Δ fixed meal bolus Δ for carbohydrate counting  0-50 −MAX(1,INT_MIN[0.1*DD(k), 0.2 MAX(1, INT_MIN[0.1*DD(k), *DD(k)]) 0.2*DD(k)]) 51-100 −MAX(1, INT_MIN[0.05*DD(k), MAX(1, INT_MIN[0.05*DD(k),0.1*DD(k)]) 0.1*DD(k)]) 101-200 (0) (0) 201-250 MAX(1,INT_MIN[0.05*DD(k), −MAX(1, INT_MIN[0.05*DD(k), 0.1*DD(k)]) 0.1*DD(k)])251-300 MAX(1, INT_MIN[0.1*DD(k), −MAX(1, INT_MIN[0.1*DD(k), 0.2*DD(k)])0.2*DD(k)]) 301+ MAX(1, INT_MIN[0.15*DD(k), −MAX(1, INT_MIN[0.15*DD(k),0.25*DD(k)]) 0.25*DD(k)])Wherein DD(k) refers to the dinner dosage component of the AIDF.

In certain embodiments, yet another tables can be used to adjust thelong acting insulin dosage component. For example while breakfast dosagecomponent or dinner dosage components may be adjusted according to theaforementioned examples, certain embodiments may use the following table4 to adjust the long acting dosage component based on breakfast glucosedata

TABLE 4 GI Δ  0-50 −MAX(1, INT_MIN[0.1*LD(k), 0.2*LD(k)])  51-100−MAX(1, INT_MIN[0.05*LD(k), 0.1*LD(k)]) 101-135 0 136-200 MAX(1,INT_MIN[0.05*LD(k), 0.1*LD(k)]) 201-250 MAX(1, INT_MIN[0.1*LD(k),0.2*LD(k)]) 251-300 MAX(1, INT_MIN[0.15*LD(k), 0.25*LD(k)]) 301+ MAX(1,INT_MIN[0.2*LD(k), 0.3*LD(k)])

Managing Population of Diabetics

In certain embodiments, it is desired to have a group of people withinsulin-treated diabetes better manage their blood glucose levels. Suchembodiments can be used to significantly reduce cost of health care. Forexample, it is well documented that high hemoglobin A1C is acontributing factor to a significantly higher chances of developingdiabetes related complications. Studies have shown that reducing apatient's A1C from 9% to 7% reduces his chances of developingretinophaty by about 76%. As nearly 80% of health care costs are due tohospitalizations, readmissions, or visits to the emergency room, it isuseful to reduce average A1C within a population as a tool to reducecosts of health care. It is also useful not to reduce A1C below acertain threshold as low A1C have been shown to be a high risk factorfor severe hypoglycemia. Since hypoglycemia is the leading cause foremergency room visits for people with insulin treated diabetes, it isuseful to reduce the rate of hypoglycemia of a given population as a wayto reduce overall costs of health care.

In certain embodiments it is desired to enroll a patient population to aservice that adjusts insulin dosage as a way to improve diabetesprognosis by reducing A1C and/or the rate of hypoglycemia leading to areduction in health care costs. For example, enrolling a group ofpatients that are 21-70 years of age and had a clinical diagnosis oftype 1 or type 2 diabetes for at least one year. In this example,patients may be excluded if they have a body mass index (BMI) ≧45 kg/m²;severe impairment of cardiac, hepatic, or renal functions;psychological, or cognitive impairment; more than two episodes of severehypoglycemia in the past year; or a history of hypoglycemia unawareness.Eligible patients can be enrolled into one of 3 treatment groups whichincluded patients with: I. suboptimally controlled type 1 diabetes(A1C≧7.4%) treated with basal-bolus insulin therapy that may incorporatecarbohydrate-counting; II. suboptimally controlled type 2 diabetes(A1C≧7.4%) treated with basal-bolus insulin therapy (withoutcarbohydrate-counting); and III. suboptimally controlled type 2 diabetes(A1C≧7.8%) treated with twice daily biphasic insulin.

In this example, it was useful to use the first 4 weeks as a baselineand allow patients to continue their pre-enrollment regimens withoutintervention. During the following 12 weeks, self-measured blood glucosereadings reported on patients' diaries can be processed weekly bycertain embodiments which recommends a new insulin dosage. Althoughgenerally encouraged to follow dosage recommendations, patients areallowed to deviate from the prescribed dosage during unusual situations(e.g. anticipated physical activity). Patients in Groups I and II areasked to test and record their capillary glucose 4 times a day beforemeals and before bedtime and patients in group III are asked to testtwice a day, before breakfast and dinner. All patients may be asked tomeasure capillary glucose during the night every 5-9 days. Informationcaptured in diaries included time-stamped scheduled and unscheduledglucose readings, insulin doses, and carbohydrate quantities (Group Ionly). Reduction of health care costs is measured by improved efficacy:defined in this example as the improvement in self measured weekly meanglucose, and reduction in A1C; and, by improved safety defined asreduction in the frequency of hypoglycemia for patients suffering from ahigh rate of hypoglycemia, e.g., more than 3 events per week, andmaintaining rate of hypoglycemia at an acceptable level, e.g., no morethan one event per week, for everyone else. In this example,hypoglycemia is defined as a blood glucose <65 mg/dl.

Using certain disclosed embodiments a patient population can be treatedto improve diabetes management and reduce health care costs by providingthem with a device that replaces their glucose meters and automaticallyuses the plurality of historic glucose data to adjust insulin therapysuch that the population reaches a better glycemic balance point.

In this example, using certain disclosed embodiments, can lead tosignificant reduction in A1C in just 12 weeks, for example from abaseline A1C of 8.4% to an A1C of 7.9%, and reduction in weekly meanglucose from a baseline of 174 mg/dl to an endpoint of 163 mg/dl. And,for patient with high frequency of hypoglycemia reducing its rate from3.2 events per week to 1.9 events per week without increasing A1C levelin a statistically significant manner. And, for patients withoutfrequent hypoglycemia reducing A1C from 8.5% at baseline to 7.8%, meanglucose from 182 mg/dl to 155 mg/dl without increasing frequency ofhypoglycemia, of 0.5 events per week at baseline, in a statisticallysignificant manner

Achieving the above results lead to reduction in the number of officevisits and/or the number of calls from patients to health careproviders, leading to short term health care costs saving. Furthermore,maintaining the above results over a period of time can lead tosignificant reduction in the development of diabetes relatedcomplications or visits to the emergency room resulting in a significanthealth care costs reduction.

In this example, reduction in mean glucose is achieved for all membersof the population as seen in FIG. 30. Using certain disclosedembodiments, significant reduction in mean glucose and hemoglobin A1Ccan be achieved with population members having type 2 diabetes as seenin FIG. 31 and FIG. 32. Better glycemic balance is achieved by reducingmean glucose for patient without frequent hypoglycemia while increasingmean glucose for patients with frequent hypoglycemia as seen in FIG. 33.Using certain disclosed embodiments it is possible not only to reducethe number of hypoglycemia events but also to shift their distributionsuch that if an hypoglycemic event occurs it is likelier to have ahigher low blood glucose level, for example above 50 mg/dl, as seen inFIG. 34. In certain embodiments, statistically significant reduction inthe frequency of hypoglycemia is achieved without an increase in A1C,while statistically significant reduction in A1C is achieved without anincrease in the frequency of hypoglycemia, as seen in FIG. 35. Incertain embodiments it is useful to increase daily total insulin dosageto achieve reduction in A1C.

In certain embodiments it is useful to achieve reduction in thefrequency of hypoglycemia by changing the distribution of insulinbetween different administration points rather than reducing the dailytotal insulin dosage, as seen in FIG. 36.

Certain embodiments are directed to methods, systems and/or devices fortreating a patient's diabetes by providing treatment guidance whereinthe frequency of hypoglycemic events is reduced without significantlyreducing the total amount of insulin used by the patient. For example, amethod for treating a patient's diabetes by providing treatmentguidance, the method comprising: storing one or more components of thepatient's insulin dosage regimen; obtaining data corresponding to thepatient's blood glucose-level measurements determined at a plurality oftimes; tagging each of the blood glucose-level measurements with anidentifier reflective of when or why the reading was obtained; anddetermining the patient's current glycemic state relative to a desiredbalance point; and determining from at least one of a plurality of thedata corresponding to the patient's blood glucose-level measurementswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen to get closerto the patient's desired balance point, without significantly reducingthe total amount of insulin used by the patient; wherein the desiredbalance point is the patient's lowest blood glucose-level within apredetermined range achievable before increasing the frequency ofhypoglycemic events above a predetermined threshold.

Certain embodiments are directed to apparatus for improving the healthof a diabetic population, wherein the frequency of hypoglycemic eventsis reduced without significantly reducing the total amount of insulinused by the patient. For example, an apparatus comprising: a processorand a computer readable medium coupled to the processor and collectivelycapable of: (a) storing one or more components of the patient's insulindosage regimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point, without significantly reducing thetotal amount of insulin used by the patient; wherein the desired balancepoint is the patient's lowest blood glucose-level within a predeterminedrange achievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Certain embodiments are directed to methods, systems and/or devices forimproving the health of a diabetic population, wherein the frequency ofhypoglycemic events is reduced without significantly reducing the totalamount of insulin used by the patients. For example, a method forimproving the health of a diabetic population, the method comprising:identifying at least one diabetic patient; treating the a least onediabetic patient to control the patient's blood glucose level; whereinthe patient's blood glucose level is controlled using a device capableof: (a) storing one or more components of the patient's insulin dosageregimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point without significantly reducing the totalamount of insulin used by the patient; wherein the desired balance pointis the patient's lowest blood glucose-level within a predetermined rangeachievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

In certain embodiments, the present disclosure comprehends systems,methods, and/or devices for optimizing the insulin dosage regimen indiabetes patients over time—such as in between clinic visits—to therebyenhance diabetes control.

As used herein with respect to certain embodiments, the term “insulindose” means and refers to the quantity of insulin taken on any singleoccasion, while the term “insulin dosage regimen” refers to and meansthe set of instructions (typically defined by the patient's physician orother healthcare professional) defining when and how much insulin totake in a given period of time and/or under certain conditions. Oneconventional insulin dosage regimen comprises several components,including a long-acting insulin dosage component, a plasma glucosecorrection factor component, and a carbohydrate ratio component. Thus,for instance, an exemplary insulin dosage regimen for a patient might beas follows: 25 units of long acting insulin at bedtime; 1 unit offast-acting insulin for every 10 grams of ingested carbohydrates; and 1unit of fast-acting insulin for every 20 mg/dL by which a patient'sblood glucose reading exceeds 120 mg/dL.

Referring to FIG. 1, which constitutes a generalized schematic thereof,of certain exemplary embodiments more particularly comprises anapparatus 1 having at least a first memory 10 for storing data inputscorresponding at least to one or more components of a patient's presentinsulin dosage regimen (whether comprising separate units of long-actingand short-acting insulin, premixed insulin, etc.) and the patient'sblood-glucose-level measurements determined at a plurality of times, aprocessor 20 operatively connected (indicated at line 11) to the atleast first memory 10, and a display 30 operatively coupled (indicatedat line 31) to the processor and operative to display at leastinformation corresponding to the patient's present insulin dosageregimen. The processor 20 is programmed at least to determine from thedata inputs corresponding to the patient's blood-glucose-levelmeasurements determined at a plurality of times whether and by how muchto vary at least one or the one or more components of the patient'spresent insulin dosage regimen. Such variation, if effected, leads to amodification of the patient's present insulin dosage regimen data asstored in the memory 10, as explained further herein. Thus, the datainputs corresponding to the one or more components of the patient'spresent insulin dosage regimen as stored in the memory device 10 will,at a starting time for employment of the apparatus, constitute aninsulin dosage regimen prescribed by a healthcare professional, butthose data inputs may subsequently be varied by operation of theapparatus (such as during the time interval between a patient's clinicvisits). In the foregoing manner, the apparatus is operative to monitorrelevant patient data with each new input of information (such as, at aminimum, the patient's blood-glucose-level measurements), therebyfacilitating the optimization of the patient's insulin dosage regimen inbetween clinic visits.

It is contemplated that the apparatus as generalized herein may beembodied in a variety of forms, including a purpose-built, PDA-likeunit, a commercially available device such as a cell-phone, IPHONE, etc.Preferably, though not necessarily, such a device would include dataentry means, such as a keypad, touch-screen interface, etc. (indicatedgenerally at the dashed box 40) for the initial input by a healthcareprofessional of data corresponding at least to a patient's presentinsulin dosage regimen (and, optionally, such additional data inputs as,for instance, the patient's present weight, defined upper and lowerpreferred limits for the patient's blood-glucose-level measurements,etc.), as well as the subsequent data inputs corresponding at least tothe patient's blood-glucose-level measurements determined at a pluralityof times (and, optionally, such additional data inputs as, for instance,the patient's present weight, the number of insulin units administeredby the patient, data corresponding to when the patient eats, thecarbohydrate content of the foodstuffs eaten, the meal type (e.g.,breakfast, lunch, dinner, snack, etc.). As shown, such data entry means40 are operatively connected (indicated at line 41) to the memory 10.

Display 30 is operative to provide a visual display to the patient,healthcare professional, etc. of pertinent information, including, byway of non-limiting example, information corresponding to the presentinsulin dosage regimen for the patient, the current insulin dose (i.e.,number of insulin units the patient needs to administer on the basis ofthe latest blood-glucose-level measurement and current insulin dosageregimen), etc. To that end, display 30 is operatively connected to theprocessor 20, as indicated by the dashed line 31.

As noted, the data entry means 40 may take the form of a touch-screen,in which case the data entry means 40 and display 30 may be combined(such as exemplified by the commercially available IPHONE (Apple, Inc.,California)).

Referring then to FIGS. 2 through 5, there are depicted representativeimages for a display 30 and a touch-screen type, combined display30/data entry means 40 exemplifying both the patient information thatmay be provided via the display, as well as the manner of data entry.

More particularly, FIG. 2 shows a display 30 providing current date/timeinformation 32 as well as the patient's current blood-glucose-levelmeasurement 33 based upon a concurrent entry of that data. FIG. 2further depicts a pair of scrolling arrows 42 by which the patient isable to scroll through a list 34 of predefined choices representing thetime of the patient's said current blood-glucose-level measurement. Asexplained further herein in association with a description of anexemplary algorithm for implementing certain embodiments, selection ofone of these choices will permit the processor to associate themeasurement data with the appropriate measurement time for more precisecontrol of the patient's insulin dosage regimen.

FIG. 3 shows a display 30 providing current date/time information 32, aswell as the presently recommended dose of short-acting insulin units35—based upon the presently defined insulin dosage regimen—for thepatient to take at lunchtime.

FIG. 4 shows a display 30 providing current date/time information 32, aswell as, according to a conventional “carbohydrate-counting” therapy,the presently recommended base (3 IUs) and additional doses (1 IU perevery 8 grams of carbohydrates ingested) of short-acting insulin units36 for the patient to take at lunchtime—all based upon the presentlydefined insulin dosage regimen.

In FIG. 5, there is shown a display 30 providing current date/timeinformation 32, as well as the presently recommended dose ofshort-acting insulin units 37—based upon the presently defined insulindosage regimen—for the patient to take at lunchtime according to adesignated amount of carbohydrates to be ingested. As further depictedin FIG. 5, a pair of scrolling arrows 42 are displayed, by which thepatient is able to scroll through a list of predefined meal choices 38,each of which will have associated therewith in the memory a number(e.g., grams) of carbohydrates. When the patient selects a meal choice,the processor is able to determine from the number of carbohydratesassociated with that meal, and the presently defined insulin dosageregimen, a recommended dose of short-acting insulin for the patient totake (in this example, 22 IUs of short-acting insulin for a lunch ofsteak and pasta).

In one embodiment thereof, shown in FIG. 6, the apparatus as describedherein in respect of FIG. 1 optionally includes a glucose meter(indicated by the dashed box 50) operatively connected (as indicated atline 51) to memory 10 to facilitate the automatic input of datacorresponding to the patient's blood-glucose-level measurements directlyto the memory 10.

Alternatively, it is contemplated that the glucose meter 50′ could beprovided as a separate unit that is capable of communicating (such asvia a cable or wirelessly, represented at line 51′) with the device 1′so as to download to the memory 10′ the patient's blood-glucose-levelmeasurements, such as shown in FIG. 7.

According to another embodiment, shown in FIG. 8, the apparatus 1″ maybe combined with an insulin pump 60″ and, optionally, a glucose meter50″ as well. According to this embodiment, the processor 20″ isoperative to determine from at least the patient's blood-glucose-levelmeasurement data (which may be automatically transferred to the memory10″ where the apparatus is provided with a glucose meter 50″, as shown,is connectable to a glucose meter so that these data may beautomatically downloaded to the memory 10″, or is provided with dataentry means 40″ so that these data may be input by the patient) whetherand by how much to vary the patient's present insulin dosage regimen.The processor 20″, which is operatively connected to the insulin pump60″ (indicated at line 61″), is operative to employ the insulin dosageregimen information to control the insulin units provided to the patientvia the pump 60″. Therefore, the processor 20″ and the pump 60″ form asemi-automatic, closed-loop system operative to automatically adjust thepump's infusion rate and profile based on at least the patient'sblood-glucose-level measurements. This will relieve the burden of havingto go to the healthcare provider to adjust the insulin pump's infusionrate and profile, as is conventionally the case. It will be appreciatedthat, further to this embodiment, the insulin pump 60″ may be operativeto transfer to the memory 10″ data corresponding to the rate at whichinsulin is delivered to the patient by the pump according to thepatient's present insulin dosage regimen. These data may be accessed bythe processor 20″ to calculate, for example, the amount of insulin unitsdelivered by the pump to the patient over a predefined period of time(e.g., 24 hours). Such data may thus be employed in certain embodimentsto more accurately determine a patient's insulin sensitivity, plasmaglucose correction factor and carbohydrate ratio, for instance.

Also further to this embodiment, the apparatus 1″ may optionally beprovided with data entry means, such as a keypad, touch-screeninterface, etc. (indicated generally at the dashed box 40″) for entry ofvarious data, including, for instance, the initial input by a healthcareprofessional of data corresponding at least to a patient's presentinsulin dosage regimen (and, optionally, such additional data inputs as,for instance, the patient's present weight, defined upper and lowerpreferred limits for the patient's blood-glucose-level measurements,etc.), as well as the subsequent data inputs corresponding at least tothe patient's blood-glucose-level measurements determined at a pluralityof times (to the extent that this information is not automaticallytransferred to the memory 10″ from the blood glucose meter 50″) and,optionally, such additional data inputs as, for instance, the patient'spresent weight, the number of insulin units administered by the patient,data corresponding to when the patient eats, the carbohydrate content ofthe foodstuffs eaten, the meal type (e.g., breakfast, lunch, dinner,snack), etc.

It is also contemplated that certain embodiments may be effected throughthe input of data by persons (e.g., patient and healthcare professional)at disparate locations, such as illustrated in FIG. 9. For instance, itis contemplated that the data inputs pertaining to at least thepatient's initial insulin dosage regimen may be entered by thehealthcare professional at a first location, in the form of a generalpurpose computer, cell phone, IPHONE, or other device 100 (a generalpurpose computer is depicted), while the subsequent data inputs (e.g.,patient blood-glucose-level readings) may be entered by the patient at asecond location, also in the form of a general purpose computer, cellphone, IPHONE, or other device 200 (a general purpose computer isdepicted), and these data communicated to a third location, in the formof a computer 300 comprising the at least first memory and theprocessor. According to this embodiment, the computers 100, 200, 300 maybe networked in any known manner (including, for instance, via theinternet). Such networking is shown diagrammatically via lines 101 and201. Thus, for instance, the system may be implemented via a healthcareprofessional/patient accessible website through which relevant data areinput and information respecting any updates to the predefined treatmentplan are communicated to the patient and healthcare professional.

Alternatively, it is contemplated that certain embodiments may beeffected through the input of data via persons (e.g., patient andhealthcare professional) at disparate locations, and wherein further oneof the persons, such as, in the illustrated example, the patient, is inpossession of a single device 200′ comprising the processor and memorycomponents, that device 200′ being adapted to receive data inputs from aperson at a disparate location. FIG. 10. This device 200′ could take anyform, including a general-purpose computer (such as illustrated), a PDA,cell-phone, purpose-built device such as heretofore described, etc.According to this embodiment, it is contemplated that the data inputspertaining to at least the patient's initial insulin dosage may beentered (for instance by the healthcare professional) at anotherlocation, such as via a general purpose computer, cell phone, or otherdevice 100′ (a general purpose computer is depicted) operative totransmit data to the device 200′, while the subsequent data inputs(e.g., patient blood-glucose-level measurements) may be entered directlyinto the device 200′. According to this embodiment, a healthcareprofessional could remotely input the patient's initial insulin dosageat a first location via the device 100′, and that data could then betransmitted to the patient's device 200′ where it would be received andstored in the memory thereof. According to a further permutation of thisembodiment, the afore described arrangement could also be reversed, suchthat the patient data inputs (e.g., patient blood-glucose-levelmeasurements) may be entered remotely, such as via a cell phone,computer, etc., at a first location and then transmitted to a remotelysituated device comprising the processor and memory components operativeto determine whether and by how much to vary the patient's presentinsulin dosage regimen. According to this further permutation,modifications to the patient's insulin dosage effected by operation ofcertain embodiments could be transmitted back to the patient via thesame, or alternate, means.

Referring again to FIG. 9, it is further contemplated that there may beprovided a glucose meter 50′″ (including, for instance, in the form ofthe device as described above in reference to FIG. 6) that can interface51′″ (wirelessly, via a hard-wire connection such as a USB cable,FIREWIRE cable, etc.) with a general purpose computer 200 at thepatient's location to download blood-glucose-level measurements fortransmission to the computer 300 at the third location. Referring alsoto FIG. 10, it is further contemplated that this glucose meter 50′″ maybe adapted to interface 51′″ (wirelessly, via a hard-wire connectionsuch as a USB cable, FIREWIRE cable, etc.) with the single device 200′,thereby downloading blood-glucose-level measurement data to that devicedirectly.

Turning now to FIG. 11, there is shown a diagram generalizing the mannerin which the certain embodiments may be implemented to optimize adiabetes patient's insulin dosage regimen.

In certain embodiments, there is initially specified, such as by ahealthcare professional, a patient insulin dosage regimen (comprised of,for instance, a carbohydrate ratio (“CHR”), a long-acting insulin dose,and a plasma glucose correction factor). Alternatively, the initialinsulin dosage regimen can be specified using published protocols forthe initiation of insulin therapy, such as, for example, the protocolspublished by the American Diabetes Association on Oct. 22, 2008. Howeverspecified, this insulin dosage regimen data is entered in the memory ofan apparatus (including according to several of the embodimentsdescribed herein), such as by a healthcare professional, in the firstinstance and before the patient has made any use of the apparatus.

Thereafter, the patient will input, or there will otherwiseautomatically be input (such as by the glucose meter) into the memory atleast data corresponding to each successive one of the patient'sblood-glucose-level measurements. Upon the input of such data, theprocessor determines, such as via the algorithm described herein,whether and by how much to vary the patient's present insulin dosageregimen. Information corresponding to this present insulin dosageregimen is then provided to the patient so that he/she may adjust theamount of insulin they administer.

According to certain exemplary embodiments, determination of whether andby how much to vary a patient's present insulin dosage regimen isundertaken both on the basis of evaluations conducted at predefined timeintervals (every 7 days, for example) as well as asynchronously to suchintervals. The asynchronous determinations will evaluate the patient'sblood-glucose-level data for safety each time a new blood-glucose-levelmeasurement is received to determine whether any urgent action,including any urgent variation to the patient's present insulin dosage,is necessary.

More particularly, each time a new patient blood-glucose-levelmeasurement is received 300 into the memory it is accessed by theprocessor and sorted and tagged according to the time of day themeasurement was received and whether or not it is associated with acertain event, e.g., pre-breakfast, bedtime, nighttime, etc. 310. Onceso sorted and tagged, the new and/or previously recordedblood-glucose-level measurements are subjected to evaluation for theneed to update on the basis of the passage of a predefined period oftime 320 measured by a counter, as well as the need to updateasynchronously for safety 330. For instance, a very low blood glucosemeasurement (e.g., below 50 mg/dL) representing a severe hypoglycemicevent or the accumulation of several low measurements in the past fewdays may lead to an update in the patient's insulin dosage regimenaccording to the step 330, while an update to that regimen may otherwisebe warranted according to the step 320 if a predefined period of time(e.g., 7 days) has elapsed since the patient's insulin dosage regimenwas last updated. In either case, the patient will be provided withinformation 340 corresponding to the present insulin dosage regimen(whether or not it has been changed) to be used in administering his/herinsulin.

Referring next to FIG. 12, there is shown a flowchart that still moreparticularly sets forth an exemplary algorithm by which certainembodiments may be implemented to optimize a diabetes patient's insulindosage regimen. According to the exemplary algorithm, the insulin dosagemodification contemplates separate units of long-acting and short-actinginsulin. However, it will be appreciated that certain embodiments areequally applicable to optimize the insulin dosage regimen of a patientwhere that dosage is in another conventional form (such as pre-mixedinsulin). It will also be understood from this specification thatcertain embodiments may be implemented otherwise than as particularlydescribed herein below.

According to a first step 400, data corresponding to a patient's newblood-glucose-level measurement is input, such as, for instance, by anyof the exemplary means mentioned above, into the at least first memory(not shown in FIG. 12). This data is accessed and evaluated (by theprocessor) at step 410 of the exemplary algorithm and sorted accordingto the time it was input.

More particularly according to this step 410, the blood-glucose-levelmeasurement data input is “tagged” with an identifier reflective of whenthe reading was input; specifically, whether it is a morning (i.e.,“fast”) measurement (herein “MPG”), a pre-lunch measurement (herein“BPG”), a pre-dinner measurement (herein “LPG”), a bedtime measurement(herein “BTPG”), or a nighttime measurement (herein “NPG”).

The “tagging” process may be facilitated using a clock internal to theprocessor (such as, for instance, the clock of a general purposecomputer) that provides an input time that can be associated with theblood-glucose-level measurement data synchronous to its entry.Alternatively, time data (i.e., “10:00 AM,” “6:00 PM,” etc.) orevent-identifying information (i.e., “lunchtime,” “dinnertime,”“bedtime,” etc.) may be input by the patient reflecting when theblood-glucose-level measurement data was taken, and such informationused to tag the blood-glucose-level measurement data. As a furtheralternative, and according to the embodiment where theblood-glucose-level measurement data are provided directly to the memoryby a glucose monitor, time data may be automatically associated with theblood-glucose-level measurement data by such glucose monitor (forinstance, by using a clock internal to that glucose monitor). It is alsocontemplated that, optionally, the user/patient may be queried (forinstance at a display) for input to confirm or modify any time-tagautomatically assigned a blood-glucose-level measurement data-input.Thus, for instance, a patient may be asked to confirm (via data entrymeans such as, for example, one or more buttons or keys, a touch-screendisplay, etc.) that the most recently input blood-glucose-levelmeasurement data reflects a pre-lunch (BPG) measurement based on thetime stamp associated with the input of the data. If the patientconfirms, then the BPG designation would remain associated with themeasurement. Otherwise, further queries of the patient may be made todetermine the appropriate time designation to associate with themeasurement.

It will be understood that any internal clock used to tag theblood-glucose-level measurement data may, as desired, be user adjustableso as to define the correct time for the time zone where the patient islocated.

Further according to the exemplary embodiment, the various categories(e.g., DPG, MPG, LPG, etc.) into which the blood-glucose-levelmeasurement data are more particularly sorted by the foregoing “tagging”process are as follows:

-   -   NPG—The data are assigned this designation when the time stamp        is between 2 AM and 4 AM.    -   MPG—The data are assigned this designation when the time stamp        is between 4 AM and 10 AM.    -   BPG—The data are assigned this designation when the time stamp        is between 10 AM and 3 PM.    -   LPG—The data are assigned this designation when the time stamp        is between 3 PM and 9 PM.    -   BTPG—The data are assigned this designation when the time stamp        is between 9 PM and 2 AM. If the BTPG data reflect a time more        than three hours after the patient's presumed dinnertime        (according to a predefined time window), then these data are        further categorized as a dinner compensation blood-glucose-level        (herein “DPG”).

According to the employment of a time stamp alone to “tag” theblood-glucose-level data inputs, it will be understood that there existsan underlying assumption that these data were in fact obtained by thepatient within the time-stamp windows specified above.

Per the exemplary embodiment, if the time stamp of a blood-glucose-levelmeasurement data-input is less than 3 hours from the measurement thatpreceded the last meal the patient had, it is considered biased andomitted unless it represents a hypoglycemic event.

According to the next step 420, the newly input blood-glucose-levelmeasurement is accessed and evaluated (by the processor) to determine ifthe input reflects a present, severe hypoglycemic event. This evaluationmay be characterized by the exemplary formula PG(t)<w, where PG(t)represents the patient's blood-glucose-level data in mg/dL, and wrepresents a predefined threshold value defining a severe hypoglycemicevent (such as, by way of non-limiting example, 50 mg/dL).

If a severe hypoglycemic event is indicated at step 420 then, accordingto the step 430, the patient's present insulin dosage regimen data (inthe memory 10 [not shown in FIG. 12]) is updated as warranted andindependent of the periodic update evaluation described further below.More particularly, the algorithm will in this step 430 asynchronously(that is, independent of the periodic update evaluation) determinewhether or not to update the patient's insulin dosage regimen on thebasis of whether the patient's input blood-glucose-level data reflectthe accumulation of several low glucose values over a short period oftime. According to the exemplary embodiment, the dosage associated withthe newly input blood-glucose-level measurement is immediatelydecreased. More specifically, for a severe hypoglycemic event at MPG,the long-acting insulin dosage is decreased by 20%; and for a severehypoglycemic event at BPG the breakfast short-acting insulin dose isdecreased by 20%.

The algorithm also at this step 430 updates a counter of hypoglycemicevents to reflect the newly-input (at step 400) blood-glucose-levelmeasurement. Notably, modifications to the patient's insulin dosageregimen according to this step 430 do not reset the timer counting tothe next periodic update evaluation. Thus, variation in the patient'sinsulin dosage regimen according to this step 430 will not prevent thealgorithm from undertaking the next periodic update evaluation.

Any such blood-glucose-level measurement is also entered into ahypoglycemic events database in the memory. In the exemplary embodiment,this is a rolling database that is not reset. Instead, the recordedhypoglycemic events expire from the database after a predefined periodof time has elapsed; essentially, once these data become irrelevant tothe patient's insulin dosage regime. Thus, by way of example only, thisdatabase may contain a record of a hypoglycemic event for 7 days.

Further according to the step 430, one or more warnings may be generatedfor display to the patient (such as via a display 30 [not shown in FIG.12]). It is contemplated that such one or more warnings would alert apatient to the fact that his/her blood-glucose-level is dangerously lowso that appropriate corrective steps (e.g., ingesting a glucose tablet)could be taken promptly. Additionally, and without limitation, such oneor more warnings may also correspond to any one or more of the followingdeterminations:

That the patient's blood-glucose-level measurement data reflect thatthere have been more than two hypoglycemic events during a predeterminedperiod of time (such as, by way of example only, in the past 7 days);that more than two drops in the patient's blood-glucose-levelmeasurements between the nighttime measurement and the morningmeasurement are greater than a predetermined amount in mg/dL (70 mg/dL,for instance); and/or that more than two drops in the patient'sblood-glucose-level measurement between the nighttime measurement andthe morning measurement are greater than a predetermined percentage(such as, for instance, 30%).

If a severe hypoglycemic event is not indicated at step 420, therecorded (in the memory 10) data inputs corresponding to the number ofpatient hypoglycemic events over a predetermined period of days areaccessed and evaluated by the processor (20, not shown) at step 440 todetermine if there have been an excessive number of regular hypoglycemicevents (e.g., a blood-glucose-level measurement between 50 mg/dL and 75mg/dL) over that predetermined period. This evaluation is directed todetermining whether the patient has experienced an excessive number ofsuch regular hypoglycemic events in absolute time and independent of theperiodic update operation as described elsewhere herein. Thisassessment, made at step 440, may be described by the following,exemplary formula:

Is(#{of events at HG}>Q) or is (#{of hypoglycemic events in the last Wdays}=Q)?

where HG represents the recorded number of hypoglycemic events, W is apredefined period of time (e.g., 3 days), and Q is a predefined numberdefining an excessive number of hypoglycemic events (e.g., 3). By way ofexample, Q may equal 3 and W may also equal 3, in which case if it isdetermined in step 440 that there were either 4 recorded hypoglycemicevents or there were 3 recorded hypoglycemic events in the last 3 days,the algorithm proceeds to step 430.

Notably, if step 440 leads to step 430, then a binary (“1” or “0”)hypoglycemic event correction “flag” is set to “1,” meaning that noincrease in the patient's insulin dosage regimen is allowed and thealgorithm jumps to step 490 (the periodic dosage update evaluationroutine). Potentially, the periodic update evaluation may concur thatany or all the parts of the insulin dosage regimen require an increasedue to the nature of blood-glucose-levels currently stored in the memory10 and the execution of the different formulas described hereafter.However, by setting the hypoglycemic event correction flag to “1,” thealgorithm will ignore any such required increase and would leave thesuggested part of the dosage unchanged. Therefore, this will lead to apotential reduction in any or all the components of the insulin dosageregimen to thus address the occurrence of the excessive number ofhypoglycemic events. Further according to this step, the timer countingto the next periodic update evaluation is reset.

In the next step 450, the recorded, time-sorted/taggedblood-glucose-level measurement data corresponding to the number ofpatient hypoglycemic events over a predetermined period of days (forexample, 7 days) are accessed and evaluated by the processor todetermine if there have been an excessive number of such hypoglycemicevents at any one or more of breakfast, lunch, dinner and/or in themorning over the predetermined period. This evaluation may becharacterized by the exemplary formula: #{HG(m)(b)(l)(d) in XX[d]}=Y?;where #HG(m)(b)(l)(d) represents the number of hypoglycemic events atany of the assigned (by the preceding step) measurement times ofmorning, bedtime, lunch or dinner over a period of XX (in the instantexample, 7) days (“[d]”), and Y represents a number of hypoglycemicevents that is predetermined to constitute a threshold sufficient tomerit adjustment of the patient's insulin dosage regimen (in the presentexample, 2 hypoglycemic events). It will be appreciated that theemployment of this step in the algorithm permits identification withgreater specificity of possible deficiencies in the patient's presentinsulin dosage regimen. Moreover, the further particularization of whenhypoglycemic events have occurred facilitates time-specific (e.g., afterlunch, at bedtime, etc.) insulin dosage regimen modifications.

If an excessive number of such hypoglycemic events is not indicated atstep 450, then the algorithm queries at step 460 whether or not it istime to update the patient's insulin dosage regimen irrespective of thenon-occurrence of hypoglycemic events, and based instead upon thepassage of a predefined interval of time (e.g., 7 days) since the needto update the patient's insulin dosage regimen was last assessed. Ifsuch an update is not indicated—i.e., because an insufficient timeinterval has passed—then no action is taken with respect to thepatient's insulin dosage and the algorithm ends (indicated by the arrowlabeled “NO”) until the next blood-glucose-level measurement data areinput.

If, however, an update is indicated by the fact that it has been 7 days(or other predefined interval) since the need to update the patient'sinsulin dosage was last evaluated, then before such update is effectedthe algorithm first determines, in step 470, if the patient's generalcondition falls within a predetermined “normal” range. Thisdetermination operation may be characterized by the exemplary formula:xxx≦E{PG}≦yyy; where xxx represents a lower bound for a desiredblood-glucose-level range for the patient, yyy represents an upper boundfor a desired blood-glucose-level range for the patient, and E{PG}represents the mean of the patient's recorded blood-glucose-levelmeasurements. According to the exemplary embodiment, the lower bound xxxmay be predefined as 80 mg/dL, and the upper bound yyy may be predefinedas 135 mg/dL.

It will be understood that the foregoing values may be other than as sospecified, being defined, for instance, according to the particularcountry in which the patient resides. Furthermore, it is contemplatedthat the upper (yyy) and lower (xxx) bounds may be defined by thepatient's healthcare professional, being entered, for instance, via dataentry means such as described elsewhere herein.

Where the patient's general condition is outside of the predetermined“normal” range, the algorithm proceeds to step 490 where the data areevaluated to determine whether it is necessary to correct the patient'slong-acting insulin dosage regimen.

Where, however, the patient's general condition is within thepredetermined “normal” range, the algorithm next (step 480) querieswhether the patient's recorded blood-glucose-level measurement datarepresent a normal (e.g., Gaussian) or abnormal distribution. This maybe characterized by the exemplary formula: −X<E{PĜ3}<X; where E{PĜ3}represents the third moment of the distribution of the recorded (in thememory) blood-glucose-level measurement data—i.e., the third root of theaverage of the cubed deviations in these data around the mean of therecorded blood-glucose-levels, and X represents a predefined limit(e.g., 5). It is contemplated that the predefined limit X should bereasonably close to 0, thus reflecting that the data (E{PĜ3}) are wellbalanced around the mean.

Thus, for example, where X is 5, the data are considered to be normalwhen the third root of the average of the cubed deviations thereofaround the mean of the recorded blood-glucose-levels is greater than −5but less than 5. Otherwise, the data are considered to be abnormal.

Where the data are determined to be normal in step 480 (indicated by thearrow labeled “YES”), then no action is taken to update the patient'sinsulin dosage regimen.

However, if in step 470 the mean of all of a patient's recordedblood-glucose-level measurement data are determined to fall outside ofthe predetermined “normal” range, then in step 490 the algorithmevaluates whether it is necessary to correct the patient's long-actinginsulin dosage regimen. This is done by evaluating whether the patient'srecorded MPG and BTPG data fall within an acceptable range or,alternatively, if there is an indication that the patient's long-actinginsulin dosage should be corrected due to low MPG blood-glucose-levelmeasurements. The determination of whether the patient's MPG and BTPGdata fall within a predetermined range may be characterized by theexemplary formula: xxy≦E{MPG}, E{BTPG}≦yyx; where xxy is a lower boundfor a desired blood-glucose-level range for the patient, yyx is an upperbound for a desired blood-glucose-level range for the patient, E{MPG}represents the mean of the patient's recorded MPG blood-glucose-levelmeasurements, and E{BTPG} represents the mean of the patient's recordedBTPG measurements. According to the exemplary embodiments, xxy may bepredefined as 80 mg/dL, while yyx may be predefined as 200 mg/dL.However, it will be understood that these values may be otherwisepredefined, including, as desired, by the patient's healthcare provider(being entered into the memory via data entry means, for instance).

If the determination in step 490 is positive, then update of thepatient's long-acting insulin dosage (step 510) is bypassed and thealgorithm proceeds to step 500, according to which the patient'sshort-acting insulin dosage (in the form of the carbohydrate ratio(“CHR”), a correction factor Δ, and the plasma glucose correction factorare each updated and the hypoglycemic correction “flag” reset to 0 (thuspermitting subsequent modification of the insulin dosage regimen at thenext evaluation thereof).

If, on the other hand, the determination in step 490 is negative, thenthe patient's long-acting insulin dosage is updated at step 510, alongwith performance of the updates specified at step 500. In either case,the process ends following such updates until new patientblood-glucose-level measurement data are input.

Updates of the long-acting insulin dosage regimen data may becharacterized by the following, exemplary formulas:

$\Delta_{up} = {{\left( {1 - {\alpha (2)}} \right)\; {floor}\; \left\{ \frac{{\alpha (1)}{{LD}(k)}}{100} \right\}} + {{\alpha (2)}\; {ceil}\; \left\{ \frac{{\alpha (1)}{{LD}(k)}}{100} \right\}}}$$\Delta_{down} = {{\left( {1 - {\alpha (2)}} \right)\; {floor}\; \left\{ \frac{{\alpha (1)}{{LD}(k)}}{200} \right\}} + {{\alpha (2)}\; {ceil}\; \left\{ \frac{{\alpha (1)}{{LD}(k)}}{200} \right\}}}$     If  E{MPG} < b₁       LD(k + 1) = LD(k) − Δ_(down)     Else       If  E{MPG} > b₂         LD(k + 1) = LD(k) + Δ_(up)       Else  if  E{MPG} > b₃        LD(k + 1) = LD(k) + Δ_(down)       End      End

where a(1) represents a percentage by which the patient's presentlong-acting insulin dosage regimen is to be varied, a(2) represents acorresponding binary value (due to the need to quantize the dosage),LD(k) represents the patient's present dosage of long-acting insulin.LD(k+1) represents the new long-acting insulin dosage, b₁, b₂, and b₃represent predetermined blood-glucose-level threshold parameters inmg/dL, and E{MPG} is the mean of the patient's recorded MPGblood-glucose-level measurements.

Since a patient's insulin dosage regimen is expressed in integers (i.e.,units of insulin), it is necessary to decide if a percent change(increase or decrease) in the present dosage regimen of long-actinginsulin that does not equate to an integer value should be the nearesthigher or lower integer. Thus, for instance, if it is necessary toincrease by 20% a patient's long-acting insulin dosage regimen from apresent regimen of 18 units, it is necessary to decide if the new dosageshould be 21 units or 22 units. In the exemplary algorithm, thisdecision is made on the basis of the patient's insulin sensitivity.

Insulin sensitivity is generally defined as the average total number ofinsulin units a patient administer per day divided by the patient weightin kilograms. More particularly, insulin sensitivity (IS(k)) accordingto the exemplary algorithm may be defined as a function of twice thepatient's total daily dosage of long-acting insulin (which may bederived from the recorded data corresponding to the patient's presentinsulin dosage regimen) divided by the patient's weight in kilograms.This is expressed in the following exemplary formula:

${{IS}(k)} = \frac{2 \cdot {{LD}(k)}}{KK}$

where KK is the patient weight in kilograms.

A patient's insulin sensitivity factor may of course be approximated byother conventional means, including without reliance on entry of datacorresponding to the patient's weight.

More particularly, the exemplary algorithm employs an insulinsensitivity correction factor (a_((2×1))(IS)))), a 2 entries vector, todetermine the percentage at which the dosage will be corrected and toeffect an appropriate rounding to the closest whole number for updatesin the patient's CHR, PGR and LD. When the patient's weight is known,this determination may be characterized by the following, exemplaryformula:

${\alpha ({IS})} = \left\{ \begin{matrix}{\begin{bmatrix}5 & 0\end{bmatrix}^{\prime},} & {{{IS}(k)} < y_{1}} \\{\begin{bmatrix}10 & 0\end{bmatrix}^{\prime},} & {y_{1} \leq {{IS}(k)} < y_{2}} \\{\begin{bmatrix}20 & 0\end{bmatrix}^{\prime},} & {y_{2} \leq {{IS}(k)} < y_{3}} \\{\begin{bmatrix}20 & 1\end{bmatrix}^{\prime},} & {y_{3} \leq {{IS}(k)}}\end{matrix} \right.$

where a(1) is a percentage value of adjustment from the present to a newinsulin dosage value, and a(2) is a binary value (i.e., 0 or 1). Thevalue of a(2) is defined by the value of IS(k) in relation to apredefined percent change value (e.g., y₁, y₂, y₃, y₄) for a(1). Thus,in the exemplary embodiment of the algorithm: Where, for example,IS(k)<0.3, the value of a(1) is 5 and the value of a(2) is 0; where0.3≦IS(k)<0.5, the value of a(1) is 10 and the value of a(2) is 0; where0.5≦IS(k)<0.7, the value of a(1) is 20 and the value of a(2) is 0; andwhere 0.7≦IS(k), the value of a(1) is 20 and the value of a(2) is 1.

When the patient weight is unknown, the algorithm will determine a usingthe following alternative: a(2) is set to “1” if the patient long actinginsulin dosage is greater than X units (where, for example X may equal50 insulin units), and the percentage by which we adjust the dosage willbe determined according to the mean of the blood-glucose-levelmeasurements currently in memory (i.e., E{PG}) by:

${\alpha (1)} = \left\{ \begin{matrix}{5,} & {w_{1} \leq {E\left\{ {PG} \right\}} < w_{2}} \\{10,} & {w_{2} \leq {E\left\{ {PG} \right\}} < w_{3}} \\{20,} & {w_{3} \leq {E\left\{ {PG} \right\}}}\end{matrix} \right.$

where w₁, w₂ and w₃ each represent a predefined blood-glucose-levelexpressed in mg/dL (thus, for example, w₁ may equal 135 mg/dL, w₂ mayequal 200 mg/dL, and w₃ may equal 280 mg/dL).

Returning to the exemplary formulas for updating the patient'slong-acting insulin dosage, in the exemplary algorithm the decision ofwhether and by how much to decrease or increase a patient's long-actinginsulin dosage regimen is based on the predetermined thresholdparameters b₁, b₂, and b₃; where, by way of example only, b₁=80 mg/dL,b₂=120 mg/dL, and b₃=200 mg/dL. More particularly, where the mean of thepatient's MPG blood-glucose-level data is less than 80 mg/dL, the newlong-acting insulin dosage (LD(k+1)) is the present long-acting insulindosage (LD(k)) minus the value of Δ_(down) (which, as shown above, is afunction of the insulin sensitivity correction factors a(1) and a(2),and the patient's long-acting insulin dosage (LD(k)) and may equal halfof Δ.sub.up). Otherwise, if the mean of the patient's MPGblood-glucose-level data is greater than 200 mg/dL, the new long-actinginsulin dosage (LD(k+1)) is the present long-acting insulin dosage(LD(k)) plus the value of the Δ_(up) (which, as shown above, is afunction of the insulin sensitivity correction factors a(1) and a(2),and the patient's long-acting insulin dosage (LD(k)). Finally, if themean of the patient's MPG blood-glucose-level data is greater than 150but less than 200, the new long-acting insulin dosage (LD(k+1)) is thepresent long-acting insulin dosage (LD(k)) plus the value of theΔ_(down).

The corrective amount Δ is calculated as a percentage of the currentlong-acting insulin dosage rounded according to a(2). In a particularexample, if a(1)=20, a(2)=0, and the current long acting insulin dosageLD(k)=58, then Δ.sub.up equals 20% of 58, which is 11.6, rounded down toΔ_(up)=11. Accordingly, the long-acting insulin dosage would be updatedto LD(k+1)=58+11=69.

It will be appreciated by reference to the foregoing that in certainembodiments no “ping-pong” effect is allowed; in other words, thepatient's long-acting insulin dosage may not be adjustable so that anytwo successive such adjusted dosages fall below and above the dosagewhich they immediately succeed. Thus, it is not permitted to have theoutcome where the latest LD update (LD(2)) is greater than the initialLD set by the healthcare professional (LD(0)), and the preceding LDupdate (LD(1)) is less than LD(0). Thus, the outcome LD(2)>LD(0)>LD(1)is not permitted in certain embodiments.

Returning to the step 450, if an excessive number of hypoglycemic eventsat any of the time-tagged blood-glucose-level measurement data forbreakfast, lunch, dinner or in the morning over the predetermined period(for instance, 7 days) are indicated from the patient's data, then atstep 520 the algorithm identifies from the recorded, time-tagged data ofhypoglycemic events when those events occurred in order to affect anysubsequently undertaken variation to the patient's insulin dosageregimen, and also sets the binary hypoglycemic correction “flag” (e.g.,“1” or “0”, where 1 represents the occurrence of too many hypoglycemicevents, and 0 represents the nonoccurrence of too many hypoglycemicevents) to 1. The presence of this “flag” in the algorithm at thisjuncture prevents subsequent increases in the patient's insulin dosageregimen in the presence of too many hypoglycemic events.

Further according to this step 520, where the blood-glucose-levelmeasurement data reflects hypoglycemic events in the morning or duringthe night, the algorithm identifies the appropriate modificationrequired to any subsequent variation of the patient's insulin dosageregimen. This may be characterized by the following, exemplary formula:If #HG events in {MPG+NTPG}=X, then reduce LD by a(1)/2; where #HG isthe number of recorded patient hypoglycemic events at the MPG andNTPG-designated blood-glucose-level measurements, X is a predefinedvalue (such as, for example, 2), LD refers to the long-acting insulindosage, and a(1) represents the afore described insulin sensitivitycorrection factor, expressed as a percentage. Thus, a(1)/2 reflects thatthe patient's long-acting insulin dosage is to be reduced only by ½ ofthe value of a(1), if at all, where the recorded hypoglycemic eventsoccur in the morning or overnight.

Further according to this step 520, where the blood-glucose-levelmeasurement data reflects hypoglycemic events during the day, thealgorithm identifies the appropriate modification required to anysubsequent variation of the patient's insulin dosage regimen. This maybe characterized by the following formula: If #HG events in {BPG or LPGor NTPG}=X, then see update 6; where #HG is the number of recordedpatient hypoglycemic events at any of the BPG, LPG or NTPG time-taggedmeasurements, X is a predefined value (for instance, 2), and “see updateΔ” refers to short-acting insulin dosage correction factor Δincorporated into the exemplary form of the algorithm, as describedherein.

Following step 520, the algorithm queries 530 whether it is time toupdate the patient's insulin dosage regimen irrespective of theoccurrence of hypoglycemic events and based upon the passage of apredefined interval of time (by way of non-limiting example, 7 days)since the need to update the patient's insulin dosage regimen was lastassessed. Thus, it is possible that a patient's insulin dosage regimenwill not be updated even though the HG correction flag has been“tripped” (indicating the occurrence of too many hypoglycemic events) ifan insufficient period of time has passed since the regimen was lastupdated.

If an insufficient period of time has passed, the process is at an end(indicated by the arrow labeled “NO”) until new blood-glucose-levelmeasurement data are input. If, on the other hand, the predefined periodof time has passed, then the algorithm proceeds to the step 490 todetermine if the long-acting insulin dosage has to be updated asdescribed before followed by the update step 500, according to which thepatient's short-acting insulin dosage (in the form of the carbohydrateratio (“CHR”)), the correction factor Δ, and plasma glucose correctionfactor are each updated and the hypoglycemic correction flag reset to 0.

According to the step 500, an update to the patient's plasma glucosecorrection factor (“PGR”) is undertaken. This may be characterized bythe following, exemplary formulas:

${{{Calculate}\mspace{20mu} {new}\mspace{14mu} {{PGR}\left( {``{NPGR}"} \right)}\text{:}\mspace{14mu} {NPGR}} = {{\frac{1700}{E\left\{ {DT} \right\}}.{Calculate}}\mspace{14mu} {difference}}},{\Delta = {{{{PGR}(k)} - {NPGR}}}}$${{If}\mspace{14mu} \frac{\Delta}{{PGR}(k)}} \leq \frac{\alpha (1)}{100}$     Δ = (1 − α(2)) floor {Δ} + α(2) ceil {Δ} Else$\mspace{65mu} {\Delta = {{\left( {1 - {\alpha (2)}} \right)\; {floor}\; \left\{ \frac{{\alpha (1)}{{PGR}(k)}}{100} \right\}} + {{\alpha (2)}\; {ceil}\; \left\{ \frac{{\alpha (1)}{{PGR}(k)}}{100} \right\}}}}$End PGR(k + 1) = PGR(k) + Δ ⋅ sign(NPGR − PGR(k))PGR(k + 1) = quant(PGR(k + 1), ZZ);Quantize  correction  to  steps  of  ZZ[mg/dL].

More particularly, the new PGR (“NPGR”) is a function of a predefinedvalue (e.g., 1700) divided by twice the patient's total daily dosage oflong-acting insulin in the present insulin dosage regimen. In theforegoing formulas, the value of this divisor is represented by E{DT},since the value representing twice the patient's daily dosage oflong-acting insulin in the present insulin dosage regimen is substitutedas an approximation for the mean of the total daily dosage of insulinadministered to the patient (which data may, optionally, be employed ifthey are input into the memory by an insulin pump, such as in theexemplary apparatus described above, or by the patient using data entrymeans). The resultant value is subtracted from the present patient PGR(“PGR(k)”) to define a difference (“Δ”). If the Δ divided by the presentPGR(k) is less than or equal to the value of a(1) divided by 100, thenthe integer value of Δ (by which new PGR (i.e., PGR(k+1)) is updated) isa function of the formula Δ=(1−a(2))floor{Δ}+a(2)ceil{Δ}, where a(2) isthe insulin sensitivity correction factor (1 or 0), “floor” is value ofΔ rounded down to the next integer, and “ceil” is the value of Δ roundedup to the next integer. If, on the other hand, the Δ divided by thepresent PGR(k) is greater than the value of a(1) divided by 100, thenthe integer value of Δ is a function of the formula

${\Delta = {{\left( {1 - {\alpha (2)}} \right)\; {floor}\; \left\{ \frac{{\alpha (1)}{{PGR}(k)}}{100} \right\}} + {{\alpha (2)}\; {ceil}\; \left\{ \frac{{\alpha (1)}{{PGR}(k)}}{100} \right\}}}},$

where a(2) is the insulin sensitivity correction factor (1 or 0), a(1)is the percent value of the insulin sensitivity correction factor,PGR(k) is the present PGR, “floor” is value of Δ rounded down to thenext integer, and “ceil” is the value of Δ rounded up to the nextinteger. According to either outcome, the new PGR (PGR(k+1)) is equal tothe present PGR (PGR(k)) plus Δ times the sign of the difference,positive or negative, of NPGR minus PGR(k).

Furthermore, it is contemplated that the new PGR will be quantized topredefined steps of mg/dL. This is represented by the exemplary formula:PGR(k+1)=quant(PGR(k+1), ZZ) PGR(k+1)=quant(PGR(k+1), ZZ); where, by wayof a non-limiting example, ZZ may equal 5.

Also according to the update step 500, updates to the patient'sshort-acting insulin dosage regimen are undertaken by modifying thecarbohydrate ratio (CHR). CHR represents the average carbohydrate toinsulin ratio that a patient needs to determine the correct dose ofinsulin to inject before each meal. This process may be characterized bythe following, exemplary formulas:

${{Calculate}\mspace{20mu} {new}\mspace{14mu} {{CHR}\left( {``{NCHR}"} \right)}},{{NCHR} = \frac{500}{E\left\{ {DT} \right\}}}$Calculate  difference, Δ = CHR(k) − NCHR${{If}\mspace{14mu} \frac{\Delta}{{CHR}(k)}} \leq \frac{\alpha (1)}{100}$     Δ = (1 − α(2)) floor  {Δ} + α(2) ceil {Δ} Else$\mspace{70mu} {\Delta = {{\left( {1 - {\alpha (2)}} \right)\; {floor}\; \left\{ \frac{{\alpha (1)}{{CHR}(k)}}{100} \right\}} + {{\alpha (2)}\; {ceil}\; \left\{ \frac{{\alpha (1)}{{CHR}(k)}}{100} \right\}}}}$End       CHR(k + 1) = CHR(k) + Δ ⋅ sign(NCHR − CHR(k))

More particularly, the new CHR (“NCHR”) is a function of a predefinedvalue (e.g., 500) divided by twice the patient's total daily dosage oflong-acting insulin in the present insulin dosage regimen. In theforegoing formulas, the value of this divisor is represented by E{DT},since the value representing twice the patient's daily dosage oflong-acting insulin in the present insulin dosage regimen is substitutedas an approximation for the mean of the total daily dosage of insulinadministered to the patient (which data may, optionally, be employed ifthey are input into the memory by an insulin pump, such as in theexemplary apparatus described above, or by the patient using data entrymeans). The resultant value is subtracted from the present patient CHR(“CHR(k)”) to define a difference (“Δ”). If the Δ divided by the presentCHR(k) is less than or equal to the value of a(1) divided by 100, thenthe integer value of A (by which new CHR (i.e., CHR(k+1)) is updated) isa function of the formula Δ=(1−a(2))floor{Δ}+a(2)ceil{Δ}, where a(2) isthe insulin sensitivity correction factor (1 or 0), “floor” is value ofΔ rounded down to the next integer, and “ceil” is the value of Δ roundedup to the next integer. If, on the other hand, the Δ divided by thepresent CHR(k) is greater than the value of a(1) divided by 100, thenthe integer value of Δ is a function of the formula

${\Delta = {{\left( {1 - {\alpha (2)}} \right)\; {floor}\; \left\{ \frac{{\alpha (1)}{{CHR}(k)}}{100} \right\}} + {{\alpha (2)}\; {ceil}\; \left\{ \frac{{\alpha (1)}{{CHR}(k)}}{100} \right\}}}},$

where a(2) is the insulin sensitivity correction factor (1 or 0), a(1)is the percent value of the insulin sensitivity correction factor,CHR(k) is the present CHR, “floor” is value of Δ rounded down to thenext integer, and “ceil” is the value of Δ rounded up to the nextinteger. According to either outcome, the new CHR (CHR(k+1)) is equal tothe present CHR (CHR(k)) plus Δ times the sign of the difference,positive or negative, of NCHR minus CHR(k).

As patients may respond differently to doses of short-acting insulindepending upon the time of day the injection is made, a different doseof insulin may be required to compensate for a similar amount ofcarbohydrates consumed for breakfast, lunch, or dinner. For example, onemay administer ‘1’ insulin unit for every ‘10’ grams of carbohydratesconsumed at lunch while administering ‘1’ insulin unit for every ‘8’grams of carbohydrates consumed at dinner. In the exemplary embodimentof the algorithm, this flexibility is achieved by the parameter Delta,δ, which is also updated in the step 500. It will be understood that thecarbohydrate to insulin ratio (CHR) as calculated above is the same forall meals. However, the actual dosage differs among meals (i.e.,breakfast, lunch, dinner) and equals CHR-δ. Therefore, the exemplaryalgorithm allows the dosage to be made more effective by slightlyaltering the CHR with δ to compensate for a patient's individualresponse to insulin at different times of the day.

Delta δ is a set of integers representing grams of carbohydrates, and ismore specifically defined as the set of values [δb, δl, δd], where “b”represents breakfast, “l” represents lunch, and “d” represents dinner.Delta, δ, may be either positive—thus reflecting that before a certainmeal it is desired to increase the insulin dose—or negative—thusreflecting that due to hypoglycemic events during the day it is desiredto decrease the insulin dose for a given meal.

Initially, it is contemplated that each δ in the set [δb, δl, δd] may bedefined by the patient's healthcare professional or constitute apredefined value (e.g., δ=[0, 0, 0] for each of [b, l, d], or [δb, δl,δd], thus reflecting that the patient's CHR is used with no alterationfor breakfast, lunch, or dinner).

The range of δ (“Rδ”) is defined as the maximum of three differences,expressed as max(|δb−δl|, |δb−δl|, |δd-δl|). In addition the algorithmdefines the minimal entry (“δ_(min)”) of the set [δb, δl, δd], expressedas min(δb, δl, δd).

Any correction to the patient's CHR can only result in a new Rδ(“Rδ(k+1)”) that is less than or equal to the greatest of the range ofthe present set of δ (Rδ (k)) or a predefined limit (D), which may, forinstance, be 2, as in the exemplary embodiment.

Against the foregoing, if the number of hypoglycemic events (HG) in agiven meal (b, l or d) over a predefined period (for example, 7 days) isequal to a predefined value (for instance, 2), and if the correspondingδb, δl, or δd is not equal to the δ_(min) or the range is 0 (R_(δ)=0),then the decrease in that δ (δb, δl, or δd) is equal to the presentvalue for that δ minus a predefined value (“d”), which may, forinstance, be 1; thus, δ_({1})=δ_({i}−)d.

Otherwise, if the corresponding δb, δl, or δd is equal to the δ.sub.minand the range is other than 0, then the decrease in that δ (e.g., δb,δl, or δd) is effected by decreasing each δ in the set (i.e., [δb, δl,or δd]) by the predefined value “d” (e.g., 1); thus, δ=δ−d (where δrefers to the entire set [δb, δl, or δd]).

If, on the other hand, the number of hypoglycemic events stored in thememory is insignificant, it may be necessary to increase Δ in one ormore of the set (i.e., [δb, δl, or δd]). To determine if an increase isdue, the algorithm looks for an unbalanced response to insulin betweenthe three meals (b, l, d). A patient's response to his/her recentshort-acting insulin dosage is considered unbalanced if the meanblood-glucose-level measurements associated with two of the three mealsfalls within a predefined acceptable range (e.g., >a₁ but <a₂; where,for instance, a₁=80 and a₂=120), while the mean of theblood-glucose-level measurements associated with the third meal fallsabove the predefined acceptable range.

If the mean for two meals falls within [a₁, a₂], while the mean of thethird meal is >a₂, then the δ values for the updated set [δb, δl, or δd]are defined by the following, exemplary formulas:

δ_(tmp)=δ;

δ_(tmp)(i)=δ_(tmp)(i)+d;

If (R _(δ-tmp) =R _(δ)) or(R _(δ-tmp) <=D), then δ=δ_(tmp)

According to the foregoing, a test set of [δb, δl, or δd], designatedδ_(tmp), is defined, wherein the value of each of δb, δl, and δd equalsthe present value of each corresponding δb, δl, and δd. The δ value inthe test set corresponding to the meal (b, l, or d) where theblood-glucose-level measurement was determined to exceed the predefinedacceptable range (e.g., >a₂) is then increased by the value “d” (e.g.,1), and the new set is accepted if it complies with one of thestatements: R_(δ-tmp)<=Rδ (i.e., is the range R_(δ) of the test set(“R_(δ-tmp)”) less than or equal to the range (R_(δ)) of the presentset; or R_(δ-tmp)<=D (i.e., is the range R.sub.Δ of the test set(“R_(δ-tmp)”) less than or equal to the predefined value “D” (e.g., 2).

The foregoing will thus yield an increase in the insulin dosage for aparticular meal if the patient's mean blood-glucose-level measurementdata are outside of a predetermined range, such as, by way of exampleonly, between a₁=80 and δ₂=120.

Further according to this step 500, the binary hypoglycemiccorrection-flag is reset to 0, reflecting that the patient's insulindosage regimen has been updated (and thus may be updated again at thenext evaluation).

It will be appreciated that the PGR and CHR values determined at step500 may optionally be employed by the processor to calculate, perconventional formulas, a “sliding scale”-type insulin dosage regimen.Such calculations may employ as a basis therefore a predefined averagenumber of carbohydrates for each meal. Alternatively, data correspondingto such information may be input into the memory by the patient usingdata entry means.

Per the exemplary algorithm as described above, it will be appreciatedthat if a hypoglycemic event causes some dosage reduction, no otherdosage can go up at the next update cycle, with respect to certainembodiments.

It should be noted that, according to certain exemplary embodiments ofthe algorithm herein described, any time a periodic evaluation of thepatient insulin dosage regimen is undertaken, the algorithm treats theinsulin dosage regimen as having been updated even if there has been nochange made to the immediately preceding insulin dosage regimen. And,moreover, any time the insulin dosage regimen is updated, whether inconsequence of a periodic update evaluation or an asynchronous update,the timer counting to the next periodic update evaluation will be resetto zero.

As noted, in operation of certain embodiments, there is initiallyspecified by a healthcare professional a patient insulin dosage regimencomprised of, for example, a long-acting insulin dose component, acarbohydrate ratio component and a plasma-glucose correction factorcomponent. This insulin dosage regimen data is entered in the memory ofan apparatus, such as by a healthcare professional, in the firstinstance and before the patient has made any use of the apparatus.Optionally, and as necessary, the internal clock of the apparatus is setfor the correct time for the time zone where the patient resides so thatthe time tags assigned to patient's blood-glucose-level measurements asthey are subsequently input into the apparatus are accurate in relationto when, in fact, the data are input (whether automatically, manually,or a combination of both). Thereafter, the patient will input, or therewill otherwise automatically be input (such as by the glucose meter)into the memory at least data corresponding to each successive one ofthe patient's blood-glucose-level measurements. Upon the input of suchdata, the processor determines, such as via the algorithm describedhereinabove, whether and by how much to vary the patient's presentinsulin dosage regimen. Information corresponding to this presentinsulin dosage regimen is then provided to the patient so that he/shemay adjust the amount of insulin they administer.

In the following, further embodiments are explained with the help ofsubsequent examples:

Example 1

A method for treating a patient's diabetes by providing treatmentguidance, the method comprising: storing one or more components of thepatient's insulin dosage regimen; obtaining data corresponding to thepatient's blood glucose-level measurements determined at a plurality oftimes; tagging each of the blood glucose-level measurements with anidentifier reflective of when or why the reading was obtained; anddetermining the patient's current glycemic state relative to a desiredbalance point; and determining from at least one of a plurality of thedata corresponding to the patient's blood glucose-level measurementswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen to get closerto the patient's desired balance point; wherein the desired balancepoint is the patient's lowest blood glucose-level within a predeterminedrange achievable before increasing the frequency of hypoglycemic eventsabove a predetermined threshold.

Example 2

The method of Example 1, wherein the adjustment to the patient's insulindosage regimen is performed in substantially real time.

Example 3

The method of Example 1 wherein an initial insulin dosage regimen isprovided by a physician or other healthcare professional

Example 4

The method of Example 1, wherein the method is performed without anyintervention from a doctor or other healthcare professional.

Example 5

The method of Example 1 wherein the patient's current balance pointchanges over time and the adjustment to patient's insulin dosage regimenis to get closer to the most recent desired balance point.

Example 6

The method of Example 5 wherein the patient's insulin dosage regimen isadjusted in a manner that dampens or prevents unstable oscillations.

Example 7

The method of Example 5 wherein the scope of the oscillations arereduced by ensuring that the current increase in the patient's insulindosage regimen is less than the previous decrease in the patient'sinsulin dosage regimen.

Example 8

The method of Example 1 wherein the identifiers reflective of when thereading was obtained are selected from Breakfast, Lunch, Dinner,Bedtime, Nighttime, and Other.

Example 9

The method of Example 8 wherein the measurements tagged as “other” areclassified based on the classification of the previous measurement andan elapsed time since the previous measurement.

Example 10

The method of Example 1 wherein the predetermined threshold is onesevere hypoglycemic event.

Example 11

The method of Example 10 wherein the severe hypoglycemic event isdefined as a blood glucose-level measurement of less than 55 mg/dL.

Example 12

The method of Example 1 wherein the hypoglycemic event is defined as ablood glucose-level measurement of less than 65 mg/dL.

Example 13

The method of Example 1 wherein the predetermined threshold is threehypoglycemic events in 24 hours.

Example 14

The method of Example 1 wherein the predetermined threshold is twohypoglycemic events for the same identifier.

Example 15

The method of Example 1 wherein the predetermined threshold is more thanthree hypoglycemic events since the current dosage has been instated.

Example 16

A method for updating a patient's insulin dosage regimen, the methodcomprising: storing one or more components of the patient's insulindosage regime; obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times;incrementing a timer based on at least one of the passage of apredetermined amount of time and the receipt of each blood glucose-levelmeasurement; tagging each of the blood glucose-level measurements withan identifier reflective of when the reading was obtained; determiningfor each of the obtained blood glucose-level measurements whether themeasurement reflects a hypoglycemic event or a severe hypoglycemicevent; and varying at least one of the one or more components in thepatient's insulin dosage regime in response to a determination that themost recent blood glucose-level measurement represents a severehypoglycemic event.

Example 17

The method of Example 16 wherein varying at least one of the one or morecomponents in the patient's insulin dosage regime is done in response toa determination that there have been an excessive number of hypoglycemicevents over a predefined period of time; and the timer is reset.

Example 18

The method of Example 16 wherein the timer indicates when to perform thestep of determining from a plurality of the data corresponding to thepatient's blood glucose-level measurements whether and by how much tovary at least one of the one or more components in the patient's presentinsulin dosage regimen; and the timer is reset.

Example 19

The method of Example 16 wherein the severe hypoglycemic event isdefined as a blood glucose-level measurement of less than 50 mg/dL.

Example 18

The method of Example 17 wherein the hypoglycemic event is defined as ablood glucose-level measurement of between 50 mg/dL and 75 mg/dL.

Example 19

The method of Example 17 wherein the severe hypoglycemic events areincluded in the determination that there have been an excessive numberof hypoglycemic events.

Example 20

The method of Example 18 wherein the timer is configured to indicatethat the step of determining from a plurality of the data correspondingto the patient's blood glucose-level measurements whether and by howmuch to vary at least one of the one or more components in the patient'spresent insulin dosage regimen after 7 days.

Example 21

The method of Example 17 wherein the excessive number of hypoglycemicevents over the predefined period of time is defined as a predeterminednumber of events in a predetermined number of days.

Example 22

The method of Example 21 wherein the excessive number of hypoglycemicevents over the predetermined period of time is selected from one of thefollowing: there have been either two hypoglycemic events with a similaridentifier; three hypoglycemic events in a twenty-four hours period; ormore than three hypoglycemic events since the current dosage wasinstated.

In the following, further embodiments of an apparatus are explained withthe help of subsequent examples:

Example 23

An apparatus for treating a patient's diabetes by providing treatmentguidance, the apparatus comprising: a processor; and a computer readablemedium coupled to the processor; wherein the combination of theprocessor and the computer readable medium are configured to: store oneor more components of the patient's insulin dosage regimen; obtain datacorresponding to the patient's blood glucose-level measurementsdetermined at a plurality of times; tag each of the blood glucose-levelmeasurements with an identifier reflective of when or why the readingwas obtained; determine the patient's current glycemic state relative toa desired balance point; and determine from at least one of a pluralityof the data corresponding to the patient's blood glucose-levelmeasurements whether and by how much to vary at least one of the one ormore components in the patient's present insulin dosage regimen to getcloser to the patient's desired balance point; wherein the desiredbalance point is the patient's lowest blood glucose-level within apredetermined range achievable before increasing the frequency ofhypoglycemic events above a predetermined threshold.

Example 24

The apparatus of Example 23, wherein the adjustment to the patient'sinsulin dosage regimen is performed in substantially real time.

Example 25

The apparatus of Example 2,3 wherein an initial insulin dosage regimenis provided by a physician or other healthcare professional.

Example 26

The apparatus of Example 23, wherein the treatment guidance is providedwithout any intervention from a doctor or other healthcare professional.

Example 27

The apparatus of Example 23, wherein the patient's current balance pointchanges over time and the adjustment to patient's insulin dosage regimenis done to get closer to the most recent desired balance point.

Example 28

The apparatus of Example 27, wherein the patient's insulin dosageregimen is adjusted in a manner that dampens, reduces, substantiallyprevents or prevents unstable dosage oscillations.

Example 29

The apparatus of Example 27, wherein the scope of the oscillations arereduced by ensuring that the current increase in the patient's insulindosage regimen is less than the previous decrease in the patient'sinsulin dosage regimen.

Example 30

The apparatus of Example 23, wherein the identifiers reflective of whenthe reading was obtained are selected from Breakfast, Lunch, Dinner,Bedtime, Nighttime, and Other.

Example 31

The apparatus of Example 30, wherein the measurements tagged as “other”are classified based on the classification of the previous measurementand an elapsed time since the previous measurement.

Example 32

The apparatus of Example 23, wherein the predetermined threshold is onesevere hypoglycemic event.

Example 33

The apparatus of Example 32, wherein the severe hypoglycemic event isdefined as a blood glucose-level measurement of less than 55 mg/dL.

Example 34

The apparatus of Example 23, wherein the hypoglycemic event is definedas a blood glucose-level measurement of less than 65 mg/dL.

Example 35

The apparatus of Example 23, wherein the predetermined threshold isthree hypoglycemic events in 24 hours.

Example 36

The apparatus of Example 23, wherein the predetermined threshold is twohypoglycemic events for the same identifier.

Example 37

The apparatus of Example 23, wherein the predetermined threshold is morethan three hypoglycemic events in seven days.

Example 38

An apparatus for updating a patient's insulin dosage regimen, theapparatus comprising: a processor; and a computer readable mediumcoupled to the processor; wherein the combination of the processor andthe computer readable medium are configured to: store one or morecomponents of the patient's insulin dosage regime; obtain datacorresponding to the patient's blood glucose-level measurementsdetermined at a plurality of times; increment a timer based on at leastone of the passage of a predetermined amount of time and the receipt ofeach blood glucose-level measurement; tag each of the bloodglucose-level measurements with an identifier reflective of when thereading was obtained; determine for each of the obtained bloodglucose-level measurements whether the measurement reflects ahypoglycemic event or a severe hypoglycemic event; vary at least one ofthe one or more components in the patient's insulin dosage regime inresponse to a determination that the most recent blood glucose-levelmeasurement represents a severe hypoglycemic event.

Example 39

The apparatus of Example 38, wherein the decision to vary at least oneof the one or more components in the patient's insulin dosage regime isdone in response to a determination that there have been an excessivenumber of hypoglycemic events over a predefined period of time; and thetimer is reset.

Example 40

The apparatus of Example 38, wherein the timer indicates when to performthe step of determining from a plurality of the data corresponding tothe patient's blood glucose-level measurements whether and by how muchto vary at least one of the one or more components in the patient'spresent insulin dosage regimen and the timer is reset.

Example 41

The apparatus of Example 38, wherein the severe hypoglycemic event isdefined as a blood glucose-level measurement of less than 50 mg/dL.

Example 42

The apparatus of Example 39, wherein the hypoglycemic event is definedas a blood glucose-level measurement of between 50 mg/dL and 75 mg/dL.

Example 43

The apparatus of Example 39, wherein the severe hypoglycemic events areincluded in the determination that there have been an excessive numberof hypoglycemic events.

Example 44

The apparatus of Example 40, wherein the timer is configured to indicatethat the step of determining from a plurality of the data correspondingto the patient's blood glucose-level measurements whether and by howmuch to vary at least one of the one or more components in the patient'spresent insulin dosage regimen after 7 days.

Example 45

The apparatus of Example 38, wherein the excessive number ofhypoglycemic events over a predefined period of time is defined as apredetermined number of events in a predetermined number of days.

Example 46

The apparatus of Example 45, wherein the predetermined number of days is7 day.

Example 47

An apparatus for improving the health of a diabetic population, theapparatus comprising: a processor and a computer readable medium coupledto the processor and collectively capable of: (a) storing one or morecomponents of the patient's insulin dosage regimen; (b) obtaining datacorresponding to the patient's blood glucose-level measurementsdetermined at a plurality of times; (c) tagging each of the bloodglucose-level measurements with an identifier reflective of when or whythe reading was obtained; (d) determining the patient's current glycemicstate relative to a desired balance point; and (e) determining from atleast one of a plurality of the data corresponding to the patient'sblood glucose-level measurements whether and by how much to vary atleast one of the one or more components in the patient's present insulindosage regimen to get closer to the patient's desired balance point;wherein the desired balance point is the patient's lowest bloodglucose-level within a predetermined range achievable before thefrequency of hypoglycemic events exceeds a predetermined threshold.

Example 48

The apparatus of Example 47, wherein the percentage of patientscontrolled to a HbA1c of less than 7.5% is at least 80%.

Example 49

The apparatus of Examples 47 or 48, wherein the percentage of patientsbrought to a HbA1c of less than 7% is at least 70%.

Example 50

The apparatus of Examples 47, 48 or 49, wherein the overall healthcaremanagement costs are reduced.

Example 51

The apparatus of Examples 47, 48 or 49, wherein the overall healthcaremanagement costs are reduced due to a reduction in the number ofhospitalizations or readmissions.

Example 52

The apparatus of Examples 47, 48 or 49, wherein the overall healthcaremanagement costs are reduced due to a reduction in the number ofemergency room visits.

Example 53

The apparatus of Examples 47 to 51, or 52 wherein there is a reductionin the frequency of hypoglycemic events within the treated population.

Example 54

The apparatus of Examples 47 to 52 or 53 wherein there the patientpopulation mean HbA1c is reduced while the frequency of hypoglycemicevents does not increase.

Example 55

The apparatus of Examples 47 to 53 or 54, wherein there is a reductionin complications within the treated population.

Example 56

The apparatus of Examples 47 to 54 or 55, wherein the percentage ofpatients developing secondary complications is reduced to no more than20% over 10 years.

Example 57

The apparatus of Examples 47 to 55 or 56, wherein at least 80% of thediabetic population being treated achieves a desired balance point in asafe and effective manner.

Example 58

The apparatus of Examples 47 to 56 or 57, wherein the method results insafe and effective adjustment of treatment in at least 80% of thetreated diabetic population over 10 years.

Example 59

The apparatus of Examples 47 to 57 or 58, wherein there is an 40%reduction in secondary complications over a 5 year period.

In the following, further embodiments of methods are explained with thehelp of subsequent examples:

Example 60

A method for improving the health of a diabetic population, the methodcomprising: treating a least one diabetic patient in the populationusing a device capable of: (a) storing one or more components of thepatient's insulin dosage regimen; (b) obtaining data corresponding tothe patient's blood glucose-level measurements determined at a pluralityof times; (c) tagging each of the blood glucose-level measurements withan identifier reflective of when or why the reading was obtained; (d)determining the patient's current glycemic state relative to a desiredbalance point; and (e) determining from at least one of a plurality ofthe data corresponding to the patient's blood glucose-level measurementswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen to get closerto the patient's desired balance point; wherein the desired balancepoint is the patient's lowest blood glucose-level within a predeterminedrange achievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Example 61

The method of Example 60, wherein the percentage of patients controlledto a HbA1c of less than 7.5% is at least 80%.

Example 62

The methods of Examples 60 or 61, wherein the percentage of patientsbrought to a HbA1c of less than 7% is at least 70%.

Example 63

The methods of Examples 60, 61 or 62, wherein the overall healthcaremanagement costs are reduced.

Example 64

The methods of Examples 60, 61 or 62, wherein the overall healthcaremanagement costs are reduced due to a reduction in the number ofhospitalizations or readmissions.

Example 65

The methods of Examples 60, 61, or 62, wherein the overall healthcaremanagement costs are reduced due to a reduction in the number ofemergency room visits.

Example 66

The methods of Examples 60, 61, 62 or 63, wherein there is a reductionin the frequency of hypoglycemic events within the treated population.

Example 67

The methods of Examples 60, 61, 62 or 63, wherein there the patientpopulation mean HbA1c is reduced while the frequency of hypoglycemicevents does not increase.

Example 68

The methods of Examples 60 to 66 or 67, wherein there is a reduction incomplications within the treated population.

Example 69

The methods of Examples 60 to 67 or 68, wherein the percentage ofpatients developing secondary complications is reduced to no more than20% over 10 years.

Example 70

The methods of Examples 60 to 68 or 69, wherein at least 80% of thediabetic population being treated achieves a desired balance point in asafe and effective manner.

Example 71

The methods of Examples 60 to 69 or 70, wherein the method results insafe and effective adjustment of treatment in at least 80% of thetreated diabetic population over 10 years.

Example 72

The methods of Examples 60 to 70 or 71, wherein there is an 40%reduction in secondary complications over a 5 year period.

Example 73

A method for improving the health of a diabetic population, the methodcomprising: identifying at least one diabetic patient; treating the aleast one diabetic patient to control the patient's blood glucose level;wherein the patient's blood glucose level is controlled using a devicecapable of: (a) storing one or more components of the patient's insulindosage regimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point; wherein the desired balance point isthe patient's lowest blood glucose-level within a predetermined rangeachievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Example 74

The method of Example 73, wherein the percentage of patients brought toa HbA1c of less than 7.5% is at least 80%.

Example 75

The methods of Examples 73 or 74, wherein the percentage of patientsbrought to a HbA1c of less than 7% is at least 70%.

Example 76

The methods of Examples 73, 74 or 75, wherein the overall healthcaremanagement costs are reduced.

Example 77

The methods of Examples 73, 74 or 75, wherein the overall healthcaremanagement costs are reduced due to a reduction in the number ofhospitalizations or readmissions.

Example 78

The methods of Examples 73, 74 or 75, wherein the overall healthcaremanagement costs are reduced due to a reduction in the number ofemergency room visits.

Example 79

The methods of Examples 73 to 77 or 78, wherein there is a reduction inthe frequency of hypoglycemic events within the treated population.

Example 80

The methods of Examples 73 to 78 or 79, wherein there the patientpopulation mean HbA1c is reduced while the frequency of hypoglycemicevents does not increase.

Example 81

The methods of Examples 73 to 79 or 80, wherein there is a reduction incomplications within the treated population.

Example 82

The methods of Examples 73 to 80 or 81, wherein the percentage ofpatients developing complications is reduced to no more than 20% over 10years.

Example 83

The methods of Examples 73 to 81 or 82, wherein at least 80% of thediabetic population being treated achieves the desired balance point ina safe and effective manner.

Example 84

The methods of Examples 73 to 82 or 83, wherein the method results insafe and effective adjustment of treatment in at least 80% of thetreated diabetic population over 10 years.

Example 85

The methods of Examples 73 to 83 or 84, wherein there is an 40%reduction in secondary complications over a 5 year period.

While the present disclosure has been described in connection withcertain embodiments, it is to be understood that the present disclosureis not to be limited to the disclosed embodiments, but on the contrary,is intended to cover various modifications and equivalent arrangements.Also, the various embodiments described herein may be implemented inconjunction with other embodiments, e.g., aspects of one embodiment maybe combined with aspects of another embodiment to realize yet otherembodiments. Further, each independent feature or component of any givenassembly may constitute an additional embodiment.

What is claimed is:
 1. A method for treating a patient's diabetes byproviding treatment guidance, the method comprising: storing one or morecomponents of the patient's insulin dosage regimen; obtaining datacorresponding to the patient's blood glucose-level measurementsdetermined at a plurality of times; tagging each of the bloodglucose-level measurements with an identifier reflective of when or whythe reading was obtained; determining the patient's current glycemicstate relative to a desired balance point; and determining from at leastone of a plurality of the data corresponding to the patient's bloodglucose-level measurements whether and by how much to vary at least oneof the one or more components in the patient's present insulin dosageregimen to get closer to the patient's desired balance point; whereinthe desired balance point is the patient's lowest blood glucose-levelwithin a predetermined range achievable before increasing the frequencyof hypoglycemic events above a predetermined threshold.
 2. The method ofclaim 1, wherein the adjustment to the patient's insulin dosage regimenis performed in substantially real time.
 3. The method of claim 1wherein an initial insulin dosage regimen is provided by a physician orother healthcare professional
 4. The method of claim 1, wherein themethod is performed without any intervention from a doctor or otherhealthcare professional.
 5. The method of claim 1 wherein the patient'scurrent balance point changes over time and the adjustment to patient'sinsulin dosage regimen is to get closer to the most recent desiredbalance point.
 6. The method of claim 5 wherein the patient's insulindosage regimen is adjusted in a manner that dampens or prevents unstableoscillations.
 7. The method of claim 5 wherein the scope of theoscillations are reduced by ensuring that the current increase in thepatient's insulin dosage regimen is less than the previous decrease inthe patient's insulin dosage regimen.
 8. The method of claim 1 whereinthe identifiers reflective of when the reading was obtained are selectedfrom Breakfast, Lunch, Dinner, Bedtime, Nighttime, and Other.
 9. Themethod of claim 8 wherein the measurements tagged as “other” areclassified based on the classification of the previous measurement andan elapsed time since the previous measurement.
 10. The method of claim1 wherein the predetermined threshold is one severe hypoglycemic event.11. The method of claim 10 wherein the severe hypoglycemic event isdefined as a blood glucose-level measurement of less than 55 mg/dL. 12.The method of claim 1 wherein the hypoglycemic event is defined as ablood glucose-level measurement of less than 65 mg/dL.
 13. The method ofclaim 1 wherein the predetermined threshold is three hypoglycemic eventsin 24 hours.
 14. The method of claim 1 wherein the predeterminedthreshold is two hypoglycemic events for the same identifier.
 15. Themethod of claim 1 wherein the predetermined threshold is more than threehypoglycemic events since the current dosage has been instated.
 16. Amethod for updating a patient's insulin dosage regimen, the methodcomprising: storing one or more components of the patient's insulindosage regime; obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times;incrementing a timer based on at least one of the passage of apredetermined amount of time and the receipt of each blood glucose-levelmeasurement; tagging each of the blood glucose-level measurements withan identifier reflective of when the reading was obtained; determiningfor each of the obtained blood glucose-level measurements whether themeasurement reflects a hypoglycemic event or a severe hypoglycemicevent; and varying at least one of the one or more components in thepatient's insulin dosage regime in response to a determination that themost recent blood glucose-level measurement represents a severehypoglycemic event.
 17. The method of claim 16 wherein varying at leastone of the one or more components in the patient's insulin dosage regimeis done in response to a determination that there have been an excessivenumber of hypoglycemic events over a predefined period of time; and thetimer is reset.
 18. The method of claim 16 wherein the timer indicateswhen to perform the step of determining from a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen; and the timer is reset.19. The method of claim 16 wherein the severe hypoglycemic event isdefined as a blood glucose-level measurement of less than 50 mg/dL. 20.The method of claim 17 wherein the hypoglycemic event is defined as ablood glucose-level measurement of between 50 mg/dL and 75 mg/dL. 21.The method of claim 17 wherein the severe hypoglycemic events areincluded in the determination that there have been an excessive numberof hypoglycemic events.
 22. The method of claim 18 wherein the timer isconfigured to indicate that the step of determining from a plurality ofthe data corresponding to the patient's blood glucose-level measurementswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen after 7 days.23. The method of claim 17 wherein the excessive number of hypoglycemicevents over the predefined period of time is defined as a predeterminednumber of events in a predetermined number of days.
 24. The method ofclaim 21 wherein the excessive number of hypoglycemic events over thepredetermined period of time is selected from one of the following:there have been either two hypoglycemic events with a similaridentifier; three hypoglycemic events in a twenty-four hours period; ormore than three hypoglycemic events since the current dosage wasinstated.